Objectives

  1. End to end analysis using R
  2. Learn the caret package for ML
  3. Learn to present the case using R Notebooks

Read in the dataset

I stored the raw files on Github, so I used RCurl with Wehrley’s method that utilizes read.csv to the fullest. It’s one of the best ways I’ve found to read in data and also set data-types at the same time. He’s done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.

library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)
readData <- function(path.name, file.name, column.types, missing.types) {
    gurl <- paste(path.name,file.name,sep="")
    download.file(gurl,file.name,method="curl",quiet = T)
    tbl_df(read.csv(file.name,colClasses=column.types,
             na.strings=missing.types))
}
Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer',   # PassengerId
                        'factor',    # Survived
                        'factor',    # Pclass
                        'character', # Name
                        'factor',    # Sex
                        'numeric',   # Age
                        'integer',   # SibSp
                        'integer',   # Parch
                        'character', # Ticket
                        'numeric',   # Fare
                        'character', # Cabin
                        'factor'     # Embarked
)
test.column.types <- train.column.types[-2]     # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)
prep_data <- function(D) {
    if (!is.null(D$Survived)) {
        D$Survived <- factor(D$Survived,
                             levels = c(1, 0),
                             labels = c('Survived', 'Dead'))
        }
    D$Pclass <- factor(D$Pclass,
                       levels = c(1, 2, 3),
                       labels = c('P1', 'P2', 'P3'))
    D$PassengerId <- NULL
    D
}
train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   891 obs. of  11 variables:
 $ Survived: Factor w/ 2 levels "Survived","Dead": 2 1 1 1 2 2 2 2 1 1 ...
 $ Pclass  : Factor w/ 3 levels "P1","P2","P3": 3 1 3 1 3 3 1 3 3 2 ...
 $ Name    : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
 $ Sex     : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
 $ Age     : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp   : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch   : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket  : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare    : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin   : chr  NA "C85" NA "C123" ...
 $ Embarked: Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...

Missing values analysis

Quick investigation of missing values can be done using the complete.cases(), and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have some missing data.

#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)

FALSE  TRUE 
 0.79  0.21

Amelia lets us graphically investigate which variables have missing data. purr::map_xxx() gives this same information numerically in a succint fashion.

missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)

#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))
Survived   Pclass     Name      Sex      Age    SibSp    Parch   Ticket     Fare    Cabin 
       0        0        0        0      177        0        0        0        0      687 
Embarked 
       2 
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)
Survived   Pclass     Name      Sex      Age    SibSp    Parch   Ticket     Fare    Cabin 
    0.00     0.00     0.00     0.00     0.20     0.00     0.00     0.00     0.00     0.77 
Embarked 
    0.00

Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.

EDA

The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:

  • Target Variable
  • Predictor Variables
    • Univariate
    • Bivariate
    • Multivariate

This gives me a structured approach towards larger datasets. My professor at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data 1. Anscombe (1973) clearly shows the importance of graphical analyses.

Target Variable

Survived is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.

round(prop.table(table(train.raw$Survived)),2)

Survived     Dead 
    0.38     0.62

Predictor Variables

Univariate & Bivariate

The first step is to look at every variable available. I prefer using the ggplot2 framework for all the visuals.

Continuous Variables

  • Age seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.

  • Fare certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.

p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)

As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We’ll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?

summary(train.raw$Fare)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    7.91   14.45   32.20   31.00  512.33

Categorical Variables

A ggplot command is iterated over for the categorical variables.2

Key takeways for the categorical variables:

  1. Pclass: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.
  2. Pclass: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.
  3. Sex: Males have a very high fatality rate. Seems like the ‘women and children’ first policy was followed during evacuation.
  4. SibSp & Parch: What’s interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the ‘women and children’ policy being followed. (Or perhaps there weren’t as many families with children on board!)
  5. Embarked: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between Embarked and Pclass shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.
get_legend<-function(myggplot){
  tmp <- ggplot_gtable(ggplot_build(myggplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
            FUN = function(x) ggplot(data = train.raw)+
                aes_string(x=x,fill='Survived')+
                geom_bar(position="dodge")+
                theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
                 cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))

# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)

Multivariate Analyses

Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find beanplots to be excellent complementary plots to boxplots (and in some cases, even better). They’re a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.3

Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?

  1. The beanplot allows us to visualize the density function of the parameter, in this case: Age. Furthermore, the length of each beanline is cumulative to the number of datapoints that exist. Rightaway, we can tell that Pclass=3 has the most data in the set, with sparser data at Pclass=1.
  2. The mean values for 1st class is higher than that for 2nd and 3rd class. The distributions of deceased and survived for 1st class are fairly similar.
  3. For 2nd and 3rd class, the survived data shows a bimodal distribution. Bumps at the 0-10 age show that children were evacuated first. This is also the reason the mean values for survived is lower.
  4. For 2nd and 3rd class, the deceased data shows a fairly normal distribution.
  5. The individual measurements (represented by black lines) represent each observation and help identify outliers much more easily than a boxplot does.
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()

beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
         border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
         main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)

A look into the SibSp and Parch variables shows something interesting. There are three regions one can identify:

  • The probability of survival is minimal for number of parents/children aboard > 3.
  • The probability of survival is minimal for number of siblings/spouses aboard > 3.
  • For SibSp<=3 and Parch<=3, there are better chances for survival.

The grouping by Pclass reveals that all the large families were 3rd class travelers. Worse access to help… lowest chance for survival.

These could be simple rules either hard coded during model building: something along the lines of: IF (SibSp>3 OR Parch >3) THEN prediction = 0, or some derived variables can be created.

ggplot(train.raw,aes(y=SibSp,x=Parch))+
    geom_jitter(aes(color=Survived,shape=Pclass))+
    theme_bw()+
    scale_shape(solid=F)+
    geom_vline(xintercept = 3,color='darkblue',lty=3)+
    geom_hline(yintercept = 3,color='darkblue',lty=3)

Data Preparation

Missing Values Imputation

Starting with the easier one first:

Embarked: The largest portion of the passengers embared at Southhampton. I’m replacing the NAs with the same. First, I create a new imputed training dataset.

summary(train.raw$Embarked)
   C    Q    S NA's 
 168   77  644    2 
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)]='S'

Names, Titles & Age:

The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles. Here’s the distribution of the titles by age. These can be used to impute the missing age values.

train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)
train.raw %>%
    na.omit() %>%
    group_by(title) %>%
    dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
    arrange(-Median_Age)
ggplot(train.raw,aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')

Grouping similar titles together, I’ve kept a few titles - Officer, Royalty, Mr, Mrs and Miss.

train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)
train.imp %>%
    group_by(title) %>%
    summarise(Median_Age=median(Age,na.rm = T))
ggplot(train.imp,aes(x=title,y=Age))+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')

Now for the missing Age values. I’m trying out two strategies to impute age, just for kicks. First, a regression tree using the rpart method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of maxdepth. RMSE stablizes at a depth of 14, with a value of 12.2.

age.predictors <- train.imp %>%
    dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
    filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
                     repeats = 5,
                     number = 200
                     )
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
                  data=age.predictors,
                  method='rpart2',
                  trControl = ctrl,
                  tuneGrid = rpartGrid
                  )
rpartFit
CART 

712 samples
  7 predictor

No pre-processing
Resampling: Bootstrapped (200 reps) 
Summary of sample sizes: 712, 712, 712, 712, 712, 712, ... 
Resampling results across tuning parameters:

  maxdepth  RMSE      Rsquared 
   4        12.91352  0.2172555
   6        12.56362  0.2600303
   8        12.37466  0.2835666
  10        12.28184  0.2961068
  12        12.23967  0.3028092
  14        12.23329  0.3046570
  16        12.24043  0.3041673
  18        12.23669  0.3045630
  20        12.23821  0.3044234

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was maxdepth = 14.
plot(rpartFit)

plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)

Another way is to run a randomforest with a search over values of mtry using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.

set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5
                     )
rfFit <- train(Age~.,
                  data=age.predictors,
                  method='rf',
                  trControl = ctrl,
                  tuneGrid = rfGrid)
rfFit
Random Forest 

712 samples
  7 predictor

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 642, 640, 642, 641, 640, 639, ... 
Resampling results across tuning parameters:

  mtry  RMSE      Rsquared 
  1     12.46449  0.3816241
  2     11.33714  0.4192503
  3     11.05166  0.4263448
  4     11.04766  0.4217797
  5     11.10717  0.4152716
  6     11.20324  0.4066238

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was mtry = 4.
plot(rfFit)

I’m going to use the randomForest model. Using the predict.train() to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of Age. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I’m going to force these to the median Age.

missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
    mutate(AgeMissing = is.na(Age),
           Age = ifelse(AgeMissing,age.predicted,Age)) %>%
    ggplot(aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')

train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)

Derived Variables

Child?: Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)

train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))

Really young, or really old?: Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.

train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)

Family related: Let’s also create some variables that talk about family sizes. What’s the total family size – continous variable TotalFam. Is the person single, part of a couple or a family? Three categorical variables for these.

train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
# train.imp$LastName <- train.imp$Name %>% str_extract(pattern = '[a-zA-Z]+(?=,)')
# train.imp$FamSize <- paste0(train.imp$TotalFam,train.imp$LastName)
# train.imp$LastName <- NULL
train.imp$Name <- NULL
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>2,1,0)

Cabin related: Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that’s important. Since lots of unknowns in the Cabin variable, all NA values are replaced by ‘U’. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that’s important too. Here, I create a bunch of categorical variables based off the original Cabin, and then remove it from the dataset.

train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))
train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'
train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0
train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)
train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULL

Ticket: Lastly, the ticket variable. I’m not sure what to make of it, so I’m keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.

train.imp$Ticket %>% table() %>% as.numeric() %>% table()
.
  1   2   3   4   5   6   7 
547  94  21  11   2   3   3

There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it’s a very loose association.

train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)

What I’m going to do is clean up the columns (remove special characters, spaces etc), then split the Ticket column into four: TicketChar, TicketNum,TicketNumLength, TicketNumStart. (Upon running the script a few times, I’ve decided to get rid of TicketNum, but I’m commenting the code for future ref). The TicketChar variable as this distribution:

train.imp %<>%
    mutate(
        Ticket = str_to_upper(Ticket) %>%
            str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
        TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
        TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
        TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
        TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+')) 
        ) %>%
     mutate(
         TicketChar = map_chr(.x=TicketChar,
                              .f=~str_split(string=.x, pattern = '',simplify = T)[1])
         ) %>%     
    mutate(
        TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
        TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
        TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar)

  A   C   F   L   P   S   U   W 
 29  47   7   4  65  65 661  13 
table(train.imp$TicketNumLen)

  1   3   4   5   6   7 
  6   7 165 246 423  44 
table(train.imp$TicketNumStart)

  0   1   2   3   4   5   6   7   8   9 
  6 231 230 365  15   9  14  15   3   3

Winzoring Variables

The fare variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.

ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)

quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95))
      10%       25%       50%       75%       95% 
 26.55000  30.92395  60.28750  93.50000 232.52395 
train.imp$Fare[train.imp$Fare>232] <- 232

Final Data Review

The dataset is now prepared for modeling. Here’s a quick review of the data so far. 29 variables in total.

train.imp %>% glimpse()
Observations: 891
Variables: 29
$ Survived       <fctr> Dead, Survived, Survived, Survived, Dead, Dead, Dead, Dead, Survive...
$ Pclass         <fctr> P3, P1, P3, P1, P3, P3, P1, P3, P3, P2, P3, P1, P3, P3, P3, P2, P3,...
$ Sex            <fctr> male, female, female, female, male, male, male, male, female, femal...
$ Age            <dbl> 22.00000, 38.00000, 26.00000, 35.00000, 35.00000, 35.04936, 54.00000...
$ SibSp          <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0,...
$ Parch          <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Fare           <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, ...
$ Embarked       <fctr> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S, C, S, S, Q...
$ title          <fctr> Mr, Mrs, Miss, Mrs, Mr, Mr, Mr, Master, Mrs, Mrs, Miss, Miss, Mr, M...
$ child          <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,...
$ almostadult    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ Young          <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Seniors        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ TotalFam       <dbl> 2, 2, 1, 2, 1, 1, 1, 5, 3, 2, 3, 1, 1, 7, 1, 1, 6, 1, 2, 1, 1, 1, 1,...
$ LargeParCh     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ LargeSibSp     <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Single         <dbl> 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,...
$ Couple         <dbl> 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ Family         <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ CabinMissing   <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ CabinCode      <chr> "U", "C", "U", "C", "U", "U", "E", "U", "U", "U", "G", "C", "U", "U"...
$ CabinNum       <dbl> 0, 8, 0, 1, 0, 0, 4, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0,...
$ TopDeck        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ MidDeck        <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ LowerDeck      <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ NumberofCabins <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ TicketNumStart <dbl> 2, 1, 3, 1, 3, 3, 1, 3, 3, 2, 9, 1, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 3,...
$ TicketNumLen   <int> 5, 5, 7, 6, 6, 6, 5, 6, 6, 6, 4, 6, 4, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6,...
$ TicketChar     <chr> "A", "P", "S", "U", "U", "U", "U", "U", "U", "U", "P", "U", "A", "U"...

Modeling

Extreme Gradient Boosting (xgboost)

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
xgbFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbFit
eXtreme Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 801, 803, 802, 802, 802, 801, ... 
Resampling results across tuning parameters:

  eta  max_depth  nrounds  ROC        Sens       Spec     
  0.3  2          2        0.8581505  0.6148067  0.9052660
  0.3  2          3        0.8677307  0.7174454  0.8699394
  0.3  2          4        0.8679025  0.7310084  0.8728485
  0.3  2          5        0.8704769  0.7262689  0.8768687
  0.3  2          6        0.8712057  0.7315630  0.8710303
  0.3  2          7        0.8718286  0.7338655  0.8706667
  0.3  3          2        0.8737598  0.7373782  0.8702963
  0.3  3          3        0.8771590  0.7403025  0.8750303
  0.3  3          4        0.8790200  0.7339160  0.8775623
  0.3  3          5        0.8804902  0.7333109  0.8815690
  0.3  3          6        0.8806026  0.7350420  0.8808620
  0.3  3          7        0.8807379  0.7256975  0.8856027
  0.3  4          2        0.8768955  0.7425882  0.8757710
  0.3  4          3        0.8805986  0.7373277  0.8794141
  0.3  4          4        0.8816917  0.7432101  0.8801145
  0.3  4          5        0.8826447  0.7444538  0.8819259
  0.3  4          6        0.8815038  0.7402185  0.8812121
  0.3  4          7        0.8804470  0.7273109  0.8833872
  0.3  5          2        0.8760521  0.7307059  0.8706397
  0.3  5          3        0.8774392  0.7290084  0.8706532
  0.3  5          4        0.8802163  0.7325210  0.8797845
  0.3  5          5        0.8793293  0.7395294  0.8775960
  0.3  5          6        0.8806162  0.7360336  0.8797778
  0.3  5          7        0.8828496  0.7377143  0.8797778
  0.3  6          2        0.8765941  0.7447059  0.8673535
  0.3  6          3        0.8772791  0.7359832  0.8706599
  0.3  6          4        0.8809166  0.7342857  0.8819057
  0.3  6          5        0.8833376  0.7430084  0.8804646
  0.3  6          6        0.8819891  0.7401008  0.8837508
  0.3  6          7        0.8839215  0.7388908  0.8844916
  0.3  7          2        0.8762051  0.7331092  0.8761010
  0.3  7          3        0.8775272  0.7423866  0.8812189
  0.3  7          4        0.8798993  0.7394286  0.8830370
  0.3  7          5        0.8803574  0.7435798  0.8852323
  0.3  7          6        0.8816307  0.7423866  0.8855960
  0.3  7          7        0.8818346  0.7429748  0.8918047
  0.5  2          2        0.8575588  0.6287899  0.8965320
  0.5  2          3        0.8669722  0.7303529  0.8714074
  0.5  2          4        0.8700492  0.7315126  0.8680943
  0.5  2          5        0.8704436  0.7315126  0.8721145
  0.5  2          6        0.8750264  0.7408571  0.8721145
  0.5  2          7        0.8764070  0.7454790  0.8786734
  0.5  3          2        0.8757504  0.7373950  0.8684714
  0.5  3          3        0.8780924  0.7397479  0.8695556
  0.5  3          4        0.8770632  0.7216134  0.8885320
  0.5  3          5        0.8781472  0.7327059  0.8819663
  0.5  3          6        0.8763049  0.7367227  0.8866734
  0.5  3          7        0.8769986  0.7308235  0.8874209
  0.5  4          2        0.8791924  0.7420000  0.8794074
  0.5  4          3        0.8805028  0.7425714  0.8739327
  0.5  4          4        0.8797489  0.7337983  0.8808889
  0.5  4          5        0.8792675  0.7314454  0.8856027
  0.5  4          6        0.8802721  0.7325042  0.8855960
  0.5  4          7        0.8808258  0.7366218  0.8866936
  0.5  5          2        0.8752125  0.7318319  0.8721010
  0.5  5          3        0.8778833  0.7303025  0.8775690
  0.5  5          4        0.8802324  0.7336807  0.8823030
  0.5  5          5        0.8792583  0.7296471  0.8819394
  0.5  5          6        0.8810007  0.7249412  0.8870438
  0.5  5          7        0.8802394  0.7249244  0.8844916
  0.5  6          2        0.8732269  0.7353613  0.8706465
  0.5  6          3        0.8761028  0.7377143  0.8801347
  0.5  6          4        0.8777128  0.7335462  0.8837845
  0.5  6          5        0.8791022  0.7341176  0.8841684
  0.5  6          6        0.8782975  0.7341176  0.8899731
  0.5  6          7        0.8771589  0.7323866  0.8892458
  0.5  7          2        0.8743506  0.7377815  0.8746330
  0.5  7          3        0.8772219  0.7419160  0.8874007
  0.5  7          4        0.8778936  0.7406723  0.8888687
  0.5  7          5        0.8804557  0.7441513  0.8903300
  0.5  7          6        0.8815295  0.7429580  0.8852189
  0.5  7          7        0.8810468  0.7377311  0.8877576

Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter 'colsample_bytree' was held
 constant at a value of 1
Tuning parameter 'min_child_weight' was held constant at a value of 1
Tuning
 parameter 'subsample' was held constant at a value of 1
ROC was used to select the optimal model using  the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree =
 1, min_child_weight = 1 and subsample = 1.
plot(xgbFit)

xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbFit,pch='|')

predict(xgbFit,type = 'raw') -> train.Class
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Extreme Gradient Boosting (xgboost) - SMOTE Sampling

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
xgbsmoteFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 

Attaching package: ‘DMwR’

The following object is masked from ‘package:plyr’:

    join
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7 
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbsmoteFit
eXtreme Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 802, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  eta  max_depth  nrounds  ROC        Sens       Spec     
  0.3  2          2        0.8422230  0.6017983  0.9041886
  0.3  2          3        0.8492899  0.6334454  0.9001886
  0.3  2          4        0.8559285  0.6701345  0.8968822
  0.3  2          5        0.8626094  0.6590252  0.9031044
  0.3  2          6        0.8654252  0.6521345  0.9060135
  0.3  2          7        0.8660548  0.6718824  0.9016296
  0.3  3          2        0.8594054  0.6228067  0.9235017
  0.3  3          3        0.8638679  0.5971429  0.9336768
  0.3  3          4        0.8659124  0.6034622  0.9326263
  0.3  3          5        0.8663275  0.6007563  0.9300404
  0.3  3          6        0.8692959  0.6066218  0.9307879
  0.3  3          7        0.8724934  0.6129412  0.9256700
  0.3  4          2        0.8552456  0.5870756  0.9348148
  0.3  4          3        0.8626897  0.5895462  0.9337104
  0.3  4          4        0.8694388  0.5895294  0.9358923
  0.3  4          5        0.8729766  0.5995126  0.9362559
  0.3  4          6        0.8755678  0.6064370  0.9366128
  0.3  4          7        0.8794687  0.6110252  0.9388081
  0.3  5          2        0.8600815  0.5929748  0.9329832
  0.3  5          3        0.8633800  0.5917983  0.9311515
  0.3  5          4        0.8689604  0.6000000  0.9347946
  0.3  5          5        0.8729573  0.6028739  0.9377104
  0.3  5          6        0.8751460  0.6105042  0.9340673
  0.3  5          7        0.8766301  0.6169580  0.9333199
  0.3  6          2        0.8633920  0.6239664  0.9191380
  0.3  6          3        0.8673741  0.6320504  0.9205859
  0.3  6          4        0.8715988  0.6268235  0.9231178
  0.3  6          5        0.8750872  0.6378992  0.9220471
  0.3  6          6        0.8761488  0.6419832  0.9209495
  0.3  6          7        0.8800014  0.6496134  0.9169630
  0.3  7          2        0.8561804  0.6192269  0.9195017
  0.3  7          3        0.8610940  0.6321008  0.9162222
  0.3  7          4        0.8646876  0.6397143  0.9176970
  0.3  7          5        0.8695185  0.6490924  0.9129428
  0.3  7          6        0.8710938  0.6572269  0.9143973
  0.3  7          7        0.8713630  0.6619496  0.9143906
  0.5  2          2        0.8490018  0.6370252  0.9012323
  0.5  2          3        0.8599325  0.6549748  0.9038047
  0.5  2          4        0.8629421  0.6410756  0.9125320
  0.5  2          5        0.8664901  0.6181849  0.9194815
  0.5  2          6        0.8674602  0.6101176  0.9202424
  0.5  2          7        0.8699278  0.6131933  0.9238451
  0.5  3          2        0.8610398  0.6136471  0.9242290
  0.5  3          3        0.8634444  0.5919160  0.9347744
  0.5  3          4        0.8695581  0.6042521  0.9296700
  0.5  3          5        0.8712536  0.5908571  0.9289495
  0.5  3          6        0.8726467  0.5941681  0.9347946
  0.5  3          7        0.8752367  0.6024370  0.9351515
  0.5  4          2        0.8626870  0.6024706  0.9329966
  0.5  4          3        0.8693973  0.5964034  0.9347879
  0.5  4          4        0.8755566  0.6059496  0.9315219
  0.5  4          5        0.8759294  0.6204874  0.9373401
  0.5  4          6        0.8739551  0.6158824  0.9373401
  0.5  4          7        0.8744622  0.6292269  0.9318855
  0.5  5          2        0.8631468  0.6064874  0.9278923
  0.5  5          3        0.8709386  0.6163361  0.9282357
  0.5  5          4        0.8742147  0.6315126  0.9267879
  0.5  5          5        0.8749953  0.6338992  0.9271582
  0.5  5          6        0.8747302  0.6426723  0.9238788
  0.5  5          7        0.8757683  0.6466891  0.9191515
  0.5  6          2        0.8625161  0.6239832  0.9213199
  0.5  6          3        0.8671210  0.6332605  0.9184108
  0.5  6          4        0.8676388  0.6419496  0.9111246
  0.5  6          5        0.8689798  0.6571765  0.9154949
  0.5  6          6        0.8728196  0.6530756  0.9125926
  0.5  6          7        0.8742729  0.6594958  0.9129495
  0.5  7          2        0.8649452  0.6513950  0.9213064
  0.5  7          3        0.8670273  0.6544034  0.9209562
  0.5  7          4        0.8748885  0.6684370  0.9198855
  0.5  7          5        0.8736510  0.6684034  0.9176835
  0.5  7          6        0.8763594  0.6748403  0.9180337
  0.5  7          7        0.8767229  0.6777983  0.9154949

Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
 was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value
 of 1
ROC was used to select the optimal model using  the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma =
 1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbsmoteFit)

xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbsmoteFit,pch='|')

predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)

Gradient Boosting (gbm)

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                                          adaptive = list(min = 5, alpha = 0.05, 
                                             method = "gls", complete = TRUE),
                     search = 'random'
                     # sampling = 'smote'
                     )
gbmGrid <- expand.grid(
   n.trees=c(500,700,900,1100),
   interaction.depth=c(1,2,3),
   shrinkage=c(0.1,0.01),
   n.minobsinnode=10
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
set.seed(1)
boostFit <- train(
    x = Dtrain,
    y = boost.train$Survived,
    trControl=ctrl,
    method='gbm',
    tuneGrid=gbmGrid
)
Loading required package: gbm
Loading required package: survival

Attaching package: ‘survival’

The following object is masked from ‘package:caret’:

    cluster

Loading required package: splines
Loading required package: parallel
Loaded gbm 2.1.3
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0027
     2        1.3203             nan     0.0100    0.0028
     3        1.3146             nan     0.0100    0.0027
     4        1.3093             nan     0.0100    0.0026
     5        1.3040             nan     0.0100    0.0026
     6        1.2991             nan     0.0100    0.0025
     7        1.2941             nan     0.0100    0.0025
     8        1.2886             nan     0.0100    0.0023
     9        1.2832             nan     0.0100    0.0024
    10        1.2782             nan     0.0100    0.0023
    20        1.2360             nan     0.0100    0.0019
    40        1.1740             nan     0.0100    0.0013
    60        1.1258             nan     0.0100    0.0009
    80        1.0870             nan     0.0100    0.0008
   100        1.0570             nan     0.0100    0.0006
   120        1.0323             nan     0.0100    0.0005
   140        1.0111             nan     0.0100    0.0003
   160        0.9930             nan     0.0100    0.0003
   180        0.9771             nan     0.0100    0.0003
   200        0.9629             nan     0.0100    0.0002
   220        0.9512             nan     0.0100    0.0002
   240        0.9406             nan     0.0100    0.0002
   260        0.9318             nan     0.0100    0.0001
   280        0.9230             nan     0.0100    0.0001
   300        0.9151             nan     0.0100    0.0001
   320        0.9079             nan     0.0100   -0.0000
   340        0.9022             nan     0.0100    0.0000
   360        0.8961             nan     0.0100    0.0001
   380        0.8906             nan     0.0100   -0.0001
   400        0.8858             nan     0.0100    0.0000
   420        0.8811             nan     0.0100   -0.0000
   440        0.8767             nan     0.0100    0.0001
   460        0.8725             nan     0.0100    0.0001
   480        0.8687             nan     0.0100    0.0000
   500        0.8647             nan     0.0100   -0.0000
   520        0.8610             nan     0.0100    0.0000
   540        0.8580             nan     0.0100   -0.0000
   560        0.8546             nan     0.0100   -0.0000
   580        0.8511             nan     0.0100    0.0000
   600        0.8481             nan     0.0100    0.0000
   620        0.8452             nan     0.0100    0.0001
   640        0.8423             nan     0.0100    0.0000
   660        0.8400             nan     0.0100   -0.0000
   680        0.8374             nan     0.0100   -0.0000
   700        0.8349             nan     0.0100    0.0000
   720        0.8325             nan     0.0100   -0.0000
   740        0.8303             nan     0.0100   -0.0001
   760        0.8280             nan     0.0100   -0.0000
   780        0.8259             nan     0.0100    0.0000
   800        0.8238             nan     0.0100   -0.0000
   820        0.8214             nan     0.0100   -0.0000
   840        0.8193             nan     0.0100   -0.0000
   860        0.8174             nan     0.0100   -0.0000
   880        0.8157             nan     0.0100   -0.0000
   900        0.8141             nan     0.0100   -0.0001
   920        0.8125             nan     0.0100   -0.0000
   940        0.8108             nan     0.0100   -0.0000
   960        0.8089             nan     0.0100   -0.0000
   980        0.8071             nan     0.0100   -0.0001
  1000        0.8054             nan     0.0100   -0.0000
  1020        0.8036             nan     0.0100   -0.0000
  1040        0.8021             nan     0.0100   -0.0000
  1060        0.8006             nan     0.0100   -0.0000
  1080        0.7992             nan     0.0100   -0.0000
  1100        0.7979             nan     0.0100   -0.0001

- Fold01.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3239             nan     0.0100    0.0036
     2        1.3168             nan     0.0100    0.0034
     3        1.3098             nan     0.0100    0.0034
     4        1.3029             nan     0.0100    0.0036
     5        1.2960             nan     0.0100    0.0031
     6        1.2897             nan     0.0100    0.0031
     7        1.2831             nan     0.0100    0.0031
     8        1.2770             nan     0.0100    0.0031
     9        1.2708             nan     0.0100    0.0031
    10        1.2648             nan     0.0100    0.0031
    20        1.2101             nan     0.0100    0.0023
    40        1.1251             nan     0.0100    0.0018
    60        1.0614             nan     0.0100    0.0013
    80        1.0137             nan     0.0100    0.0006
   100        0.9779             nan     0.0100    0.0007
   120        0.9491             nan     0.0100    0.0004
   140        0.9253             nan     0.0100    0.0004
   160        0.9064             nan     0.0100    0.0002
   180        0.8910             nan     0.0100    0.0002
   200        0.8775             nan     0.0100    0.0003
   220        0.8667             nan     0.0100    0.0002
   240        0.8575             nan     0.0100   -0.0000
   260        0.8485             nan     0.0100    0.0002
   280        0.8407             nan     0.0100    0.0002
   300        0.8328             nan     0.0100    0.0000
   320        0.8258             nan     0.0100   -0.0001
   340        0.8192             nan     0.0100    0.0001
   360        0.8131             nan     0.0100   -0.0003
   380        0.8072             nan     0.0100   -0.0000
   400        0.8019             nan     0.0100    0.0000
   420        0.7969             nan     0.0100   -0.0001
   440        0.7923             nan     0.0100   -0.0001
   460        0.7883             nan     0.0100   -0.0000
   480        0.7846             nan     0.0100   -0.0000
   500        0.7810             nan     0.0100   -0.0000
   520        0.7773             nan     0.0100   -0.0001
   540        0.7740             nan     0.0100   -0.0001
   560        0.7702             nan     0.0100   -0.0000
   580        0.7668             nan     0.0100   -0.0001
   600        0.7635             nan     0.0100   -0.0001
   620        0.7602             nan     0.0100   -0.0000
   640        0.7568             nan     0.0100   -0.0001
   660        0.7541             nan     0.0100   -0.0002
   680        0.7514             nan     0.0100   -0.0000
   700        0.7486             nan     0.0100   -0.0001
   720        0.7460             nan     0.0100   -0.0000
   740        0.7434             nan     0.0100   -0.0002
   760        0.7410             nan     0.0100    0.0000
   780        0.7378             nan     0.0100   -0.0001
   800        0.7353             nan     0.0100   -0.0002
   820        0.7329             nan     0.0100   -0.0000
   840        0.7308             nan     0.0100   -0.0001
   860        0.7285             nan     0.0100    0.0000
   880        0.7264             nan     0.0100   -0.0000
   900        0.7240             nan     0.0100   -0.0001
   920        0.7221             nan     0.0100   -0.0001
   940        0.7197             nan     0.0100   -0.0000
   960        0.7176             nan     0.0100   -0.0001
   980        0.7156             nan     0.0100    0.0000
  1000        0.7135             nan     0.0100   -0.0000
  1020        0.7113             nan     0.0100   -0.0001
  1040        0.7093             nan     0.0100   -0.0001
  1060        0.7076             nan     0.0100   -0.0001
  1080        0.7057             nan     0.0100   -0.0002
  1100        0.7037             nan     0.0100   -0.0001

- Fold01.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3231             nan     0.0100    0.0040
     2        1.3150             nan     0.0100    0.0039
     3        1.3068             nan     0.0100    0.0038
     4        1.2990             nan     0.0100    0.0037
     5        1.2914             nan     0.0100    0.0035
     6        1.2843             nan     0.0100    0.0038
     7        1.2771             nan     0.0100    0.0034
     8        1.2696             nan     0.0100    0.0037
     9        1.2627             nan     0.0100    0.0032
    10        1.2558             nan     0.0100    0.0032
    20        1.1946             nan     0.0100    0.0027
    40        1.0991             nan     0.0100    0.0021
    60        1.0308             nan     0.0100    0.0012
    80        0.9804             nan     0.0100    0.0009
   100        0.9408             nan     0.0100    0.0006
   120        0.9115             nan     0.0100    0.0006
   140        0.8876             nan     0.0100    0.0004
   160        0.8686             nan     0.0100    0.0002
   180        0.8521             nan     0.0100    0.0002
   200        0.8376             nan     0.0100    0.0003
   220        0.8248             nan     0.0100   -0.0000
   240        0.8141             nan     0.0100   -0.0001
   260        0.8043             nan     0.0100   -0.0000
   280        0.7955             nan     0.0100    0.0001
   300        0.7880             nan     0.0100   -0.0000
   320        0.7806             nan     0.0100    0.0000
   340        0.7743             nan     0.0100   -0.0000
   360        0.7687             nan     0.0100   -0.0000
   380        0.7630             nan     0.0100   -0.0001
   400        0.7571             nan     0.0100    0.0001
   420        0.7519             nan     0.0100    0.0001
   440        0.7470             nan     0.0100   -0.0001
   460        0.7424             nan     0.0100   -0.0001
   480        0.7375             nan     0.0100   -0.0000
   500        0.7327             nan     0.0100   -0.0001
   520        0.7285             nan     0.0100   -0.0001
   540        0.7244             nan     0.0100    0.0000
   560        0.7202             nan     0.0100   -0.0001
   580        0.7162             nan     0.0100   -0.0001
   600        0.7122             nan     0.0100   -0.0000
   620        0.7086             nan     0.0100   -0.0001
   640        0.7053             nan     0.0100   -0.0001
   660        0.7017             nan     0.0100   -0.0001
   680        0.6982             nan     0.0100   -0.0001
   700        0.6947             nan     0.0100   -0.0001
   720        0.6914             nan     0.0100   -0.0000
   740        0.6883             nan     0.0100   -0.0001
   760        0.6848             nan     0.0100   -0.0001
   780        0.6819             nan     0.0100   -0.0001
   800        0.6793             nan     0.0100   -0.0001
   820        0.6765             nan     0.0100   -0.0002
   840        0.6740             nan     0.0100   -0.0001
   860        0.6712             nan     0.0100   -0.0001
   880        0.6691             nan     0.0100   -0.0001
   900        0.6668             nan     0.0100   -0.0001
   920        0.6640             nan     0.0100   -0.0001
   940        0.6616             nan     0.0100   -0.0001
   960        0.6589             nan     0.0100   -0.0000
   980        0.6565             nan     0.0100   -0.0002
  1000        0.6540             nan     0.0100   -0.0001
  1020        0.6516             nan     0.0100   -0.0000
  1040        0.6490             nan     0.0100   -0.0000
  1060        0.6468             nan     0.0100   -0.0002
  1080        0.6440             nan     0.0100    0.0000
  1100        0.6417             nan     0.0100   -0.0001

- Fold01.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2729             nan     0.1000    0.0270
     2        1.2315             nan     0.1000    0.0211
     3        1.1980             nan     0.1000    0.0170
     4        1.1673             nan     0.1000    0.0150
     5        1.1433             nan     0.1000    0.0091
     6        1.1200             nan     0.1000    0.0113
     7        1.1017             nan     0.1000    0.0103
     8        1.0840             nan     0.1000    0.0084
     9        1.0682             nan     0.1000    0.0067
    10        1.0527             nan     0.1000    0.0067
    20        0.9580             nan     0.1000    0.0022
    40        0.8807             nan     0.1000   -0.0003
    60        0.8422             nan     0.1000   -0.0000
    80        0.8192             nan     0.1000    0.0002
   100        0.8026             nan     0.1000   -0.0003
   120        0.7894             nan     0.1000   -0.0005
   140        0.7782             nan     0.1000   -0.0006
   160        0.7690             nan     0.1000   -0.0007
   180        0.7635             nan     0.1000   -0.0004
   200        0.7558             nan     0.1000   -0.0006
   220        0.7498             nan     0.1000   -0.0010
   240        0.7446             nan     0.1000   -0.0014
   260        0.7375             nan     0.1000    0.0001
   280        0.7330             nan     0.1000   -0.0002
   300        0.7285             nan     0.1000   -0.0004
   320        0.7253             nan     0.1000   -0.0011
   340        0.7232             nan     0.1000   -0.0004
   360        0.7191             nan     0.1000   -0.0006
   380        0.7157             nan     0.1000   -0.0011
   400        0.7115             nan     0.1000   -0.0007
   420        0.7079             nan     0.1000   -0.0007
   440        0.7054             nan     0.1000   -0.0011
   460        0.7030             nan     0.1000   -0.0011
   480        0.7004             nan     0.1000   -0.0011
   500        0.6977             nan     0.1000   -0.0008
   520        0.6957             nan     0.1000   -0.0008
   540        0.6921             nan     0.1000   -0.0005
   560        0.6895             nan     0.1000   -0.0008
   580        0.6881             nan     0.1000   -0.0012
   600        0.6863             nan     0.1000   -0.0007
   620        0.6842             nan     0.1000   -0.0007
   640        0.6822             nan     0.1000   -0.0009
   660        0.6813             nan     0.1000   -0.0008
   680        0.6775             nan     0.1000   -0.0007
   700        0.6744             nan     0.1000   -0.0003
   720        0.6722             nan     0.1000   -0.0008
   740        0.6723             nan     0.1000   -0.0012
   760        0.6691             nan     0.1000   -0.0012
   780        0.6673             nan     0.1000   -0.0005
   800        0.6655             nan     0.1000   -0.0011
   820        0.6643             nan     0.1000   -0.0008
   840        0.6610             nan     0.1000   -0.0004
   860        0.6591             nan     0.1000   -0.0004
   880        0.6569             nan     0.1000   -0.0006
   900        0.6555             nan     0.1000   -0.0008
   920        0.6546             nan     0.1000   -0.0008
   940        0.6536             nan     0.1000   -0.0005
   960        0.6519             nan     0.1000   -0.0010
   980        0.6504             nan     0.1000   -0.0006
  1000        0.6485             nan     0.1000   -0.0007
  1020        0.6463             nan     0.1000   -0.0004
  1040        0.6454             nan     0.1000   -0.0009
  1060        0.6443             nan     0.1000   -0.0008
  1080        0.6428             nan     0.1000   -0.0002
  1100        0.6410             nan     0.1000   -0.0013

- Fold01.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2634             nan     0.1000    0.0317
     2        1.2062             nan     0.1000    0.0286
     3        1.1578             nan     0.1000    0.0246
     4        1.1170             nan     0.1000    0.0192
     5        1.0853             nan     0.1000    0.0165
     6        1.0566             nan     0.1000    0.0137
     7        1.0313             nan     0.1000    0.0103
     8        1.0089             nan     0.1000    0.0113
     9        0.9901             nan     0.1000    0.0080
    10        0.9713             nan     0.1000    0.0079
    20        0.8776             nan     0.1000    0.0020
    40        0.8043             nan     0.1000   -0.0002
    60        0.7684             nan     0.1000   -0.0029
    80        0.7388             nan     0.1000   -0.0011
   100        0.7184             nan     0.1000   -0.0027
   120        0.6993             nan     0.1000   -0.0010
   140        0.6846             nan     0.1000   -0.0006
   160        0.6711             nan     0.1000   -0.0013
   180        0.6576             nan     0.1000   -0.0006
   200        0.6473             nan     0.1000   -0.0006
   220        0.6330             nan     0.1000   -0.0020
   240        0.6203             nan     0.1000   -0.0006
   260        0.6074             nan     0.1000   -0.0010
   280        0.5965             nan     0.1000   -0.0011
   300        0.5862             nan     0.1000   -0.0004
   320        0.5772             nan     0.1000   -0.0005
   340        0.5695             nan     0.1000   -0.0013
   360        0.5625             nan     0.1000   -0.0011
   380        0.5543             nan     0.1000   -0.0018
   400        0.5489             nan     0.1000   -0.0006
   420        0.5418             nan     0.1000   -0.0002
   440        0.5335             nan     0.1000   -0.0019
   460        0.5252             nan     0.1000   -0.0009
   480        0.5172             nan     0.1000   -0.0018
   500        0.5105             nan     0.1000   -0.0012
   520        0.5063             nan     0.1000   -0.0018
   540        0.4998             nan     0.1000   -0.0008
   560        0.4935             nan     0.1000   -0.0003
   580        0.4891             nan     0.1000   -0.0010
   600        0.4814             nan     0.1000   -0.0008
   620        0.4774             nan     0.1000   -0.0009
   640        0.4733             nan     0.1000   -0.0012
   660        0.4697             nan     0.1000   -0.0016
   680        0.4640             nan     0.1000   -0.0004
   700        0.4592             nan     0.1000   -0.0009
   720        0.4543             nan     0.1000   -0.0006
   740        0.4493             nan     0.1000   -0.0011
   760        0.4461             nan     0.1000   -0.0006
   780        0.4421             nan     0.1000   -0.0006
   800        0.4390             nan     0.1000   -0.0005
   820        0.4351             nan     0.1000   -0.0015
   840        0.4306             nan     0.1000   -0.0007
   860        0.4276             nan     0.1000   -0.0008
   880        0.4228             nan     0.1000   -0.0006
   900        0.4190             nan     0.1000   -0.0008
   920        0.4162             nan     0.1000   -0.0010
   940        0.4132             nan     0.1000   -0.0009
   960        0.4086             nan     0.1000   -0.0008
   980        0.4061             nan     0.1000   -0.0012
  1000        0.4030             nan     0.1000   -0.0012
  1020        0.3990             nan     0.1000   -0.0011
  1040        0.3967             nan     0.1000   -0.0009
  1060        0.3941             nan     0.1000   -0.0010
  1080        0.3912             nan     0.1000   -0.0009
  1100        0.3892             nan     0.1000   -0.0010

- Fold01.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2563             nan     0.1000    0.0358
     2        1.1929             nan     0.1000    0.0300
     3        1.1428             nan     0.1000    0.0246
     4        1.0966             nan     0.1000    0.0214
     5        1.0585             nan     0.1000    0.0194
     6        1.0238             nan     0.1000    0.0166
     7        0.9955             nan     0.1000    0.0122
     8        0.9727             nan     0.1000    0.0102
     9        0.9511             nan     0.1000    0.0099
    10        0.9378             nan     0.1000    0.0010
    20        0.8411             nan     0.1000    0.0022
    40        0.7644             nan     0.1000    0.0000
    60        0.7272             nan     0.1000   -0.0021
    80        0.6917             nan     0.1000   -0.0012
   100        0.6629             nan     0.1000   -0.0008
   120        0.6398             nan     0.1000   -0.0012
   140        0.6170             nan     0.1000   -0.0007
   160        0.5972             nan     0.1000   -0.0001
   180        0.5803             nan     0.1000   -0.0006
   200        0.5631             nan     0.1000   -0.0010
   220        0.5480             nan     0.1000   -0.0011
   240        0.5326             nan     0.1000   -0.0019
   260        0.5209             nan     0.1000   -0.0012
   280        0.5068             nan     0.1000   -0.0010
   300        0.4943             nan     0.1000   -0.0009
   320        0.4859             nan     0.1000   -0.0010
   340        0.4740             nan     0.1000   -0.0005
   360        0.4634             nan     0.1000   -0.0004
   380        0.4522             nan     0.1000   -0.0021
   400        0.4411             nan     0.1000   -0.0009
   420        0.4341             nan     0.1000   -0.0008
   440        0.4265             nan     0.1000   -0.0009
   460        0.4173             nan     0.1000   -0.0005
   480        0.4100             nan     0.1000   -0.0016
   500        0.4039             nan     0.1000   -0.0012
   520        0.3978             nan     0.1000   -0.0008
   540        0.3921             nan     0.1000   -0.0013
   560        0.3841             nan     0.1000   -0.0011
   580        0.3765             nan     0.1000   -0.0012
   600        0.3672             nan     0.1000   -0.0008
   620        0.3623             nan     0.1000   -0.0014
   640        0.3552             nan     0.1000   -0.0005
   660        0.3507             nan     0.1000   -0.0005
   680        0.3473             nan     0.1000   -0.0006
   700        0.3405             nan     0.1000   -0.0011
   720        0.3354             nan     0.1000   -0.0003
   740        0.3300             nan     0.1000   -0.0018
   760        0.3269             nan     0.1000   -0.0007
   780        0.3234             nan     0.1000   -0.0009
   800        0.3175             nan     0.1000   -0.0009
   820        0.3132             nan     0.1000   -0.0007
   840        0.3087             nan     0.1000   -0.0004
   860        0.3045             nan     0.1000   -0.0007
   880        0.3008             nan     0.1000   -0.0011
   900        0.2973             nan     0.1000   -0.0008
   920        0.2940             nan     0.1000   -0.0004
   940        0.2906             nan     0.1000   -0.0006
   960        0.2859             nan     0.1000   -0.0009
   980        0.2829             nan     0.1000   -0.0013
  1000        0.2786             nan     0.1000   -0.0014
  1020        0.2764             nan     0.1000   -0.0005
  1040        0.2725             nan     0.1000   -0.0007
  1060        0.2695             nan     0.1000   -0.0002
  1080        0.2658             nan     0.1000   -0.0005
  1100        0.2637             nan     0.1000   -0.0011

- Fold01.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3268             nan     0.0100    0.0029
     2        1.3211             nan     0.0100    0.0028
     3        1.3153             nan     0.0100    0.0027
     4        1.3100             nan     0.0100    0.0027
     5        1.3045             nan     0.0100    0.0027
     6        1.2989             nan     0.0100    0.0025
     7        1.2932             nan     0.0100    0.0025
     8        1.2885             nan     0.0100    0.0025
     9        1.2836             nan     0.0100    0.0025
    10        1.2785             nan     0.0100    0.0024
    20        1.2341             nan     0.0100    0.0019
    40        1.1666             nan     0.0100    0.0013
    60        1.1206             nan     0.0100    0.0010
    80        1.0849             nan     0.0100    0.0007
   100        1.0570             nan     0.0100    0.0006
   120        1.0344             nan     0.0100    0.0005
   140        1.0148             nan     0.0100    0.0004
   160        0.9973             nan     0.0100    0.0003
   180        0.9824             nan     0.0100    0.0002
   200        0.9695             nan     0.0100    0.0002
   220        0.9575             nan     0.0100    0.0002
   240        0.9475             nan     0.0100    0.0002
   260        0.9384             nan     0.0100    0.0001
   280        0.9298             nan     0.0100    0.0001
   300        0.9224             nan     0.0100    0.0001
   320        0.9152             nan     0.0100    0.0001
   340        0.9088             nan     0.0100    0.0001
   360        0.9028             nan     0.0100   -0.0000
   380        0.8972             nan     0.0100    0.0000
   400        0.8921             nan     0.0100    0.0000
   420        0.8872             nan     0.0100    0.0000
   440        0.8829             nan     0.0100   -0.0000
   460        0.8784             nan     0.0100    0.0001
   480        0.8744             nan     0.0100    0.0000
   500        0.8705             nan     0.0100   -0.0000
   520        0.8667             nan     0.0100    0.0000
   540        0.8631             nan     0.0100    0.0001
   560        0.8598             nan     0.0100    0.0000
   580        0.8564             nan     0.0100   -0.0000
   600        0.8533             nan     0.0100    0.0000
   620        0.8501             nan     0.0100   -0.0000
   640        0.8471             nan     0.0100    0.0000
   660        0.8445             nan     0.0100   -0.0001
   680        0.8417             nan     0.0100   -0.0000
   700        0.8391             nan     0.0100   -0.0000
   720        0.8365             nan     0.0100   -0.0001
   740        0.8342             nan     0.0100    0.0000
   760        0.8317             nan     0.0100    0.0000
   780        0.8294             nan     0.0100   -0.0000
   800        0.8274             nan     0.0100    0.0000
   820        0.8253             nan     0.0100    0.0000
   840        0.8235             nan     0.0100   -0.0000
   860        0.8217             nan     0.0100   -0.0000
   880        0.8202             nan     0.0100   -0.0000
   900        0.8184             nan     0.0100   -0.0000
   920        0.8168             nan     0.0100   -0.0001
   940        0.8149             nan     0.0100    0.0000
   960        0.8134             nan     0.0100   -0.0000
   980        0.8118             nan     0.0100   -0.0001
  1000        0.8104             nan     0.0100   -0.0001
  1020        0.8088             nan     0.0100   -0.0000
  1040        0.8075             nan     0.0100   -0.0001
  1060        0.8059             nan     0.0100   -0.0000
  1080        0.8046             nan     0.0100   -0.0001
  1100        0.8030             nan     0.0100   -0.0001

- Fold02.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3251             nan     0.0100    0.0035
     2        1.3180             nan     0.0100    0.0034
     3        1.3110             nan     0.0100    0.0032
     4        1.3045             nan     0.0100    0.0032
     5        1.2981             nan     0.0100    0.0030
     6        1.2913             nan     0.0100    0.0032
     7        1.2851             nan     0.0100    0.0032
     8        1.2788             nan     0.0100    0.0030
     9        1.2726             nan     0.0100    0.0028
    10        1.2665             nan     0.0100    0.0030
    20        1.2126             nan     0.0100    0.0025
    40        1.1267             nan     0.0100    0.0018
    60        1.0652             nan     0.0100    0.0012
    80        1.0180             nan     0.0100    0.0010
   100        0.9832             nan     0.0100    0.0007
   120        0.9552             nan     0.0100    0.0006
   140        0.9330             nan     0.0100    0.0002
   160        0.9150             nan     0.0100    0.0002
   180        0.9003             nan     0.0100    0.0001
   200        0.8875             nan     0.0100    0.0001
   220        0.8753             nan     0.0100    0.0003
   240        0.8651             nan     0.0100    0.0000
   260        0.8556             nan     0.0100   -0.0001
   280        0.8472             nan     0.0100    0.0001
   300        0.8384             nan     0.0100    0.0001
   320        0.8308             nan     0.0100    0.0000
   340        0.8238             nan     0.0100    0.0002
   360        0.8175             nan     0.0100   -0.0000
   380        0.8125             nan     0.0100   -0.0000
   400        0.8069             nan     0.0100    0.0001
   420        0.8021             nan     0.0100    0.0000
   440        0.7978             nan     0.0100    0.0000
   460        0.7920             nan     0.0100   -0.0000
   480        0.7874             nan     0.0100   -0.0000
   500        0.7834             nan     0.0100   -0.0001
   520        0.7796             nan     0.0100   -0.0001
   540        0.7757             nan     0.0100   -0.0000
   560        0.7722             nan     0.0100   -0.0001
   580        0.7686             nan     0.0100    0.0000
   600        0.7650             nan     0.0100   -0.0000
   620        0.7619             nan     0.0100   -0.0000
   640        0.7587             nan     0.0100    0.0000
   660        0.7558             nan     0.0100   -0.0002
   680        0.7529             nan     0.0100   -0.0001
   700        0.7502             nan     0.0100   -0.0001
   720        0.7475             nan     0.0100   -0.0000
   740        0.7449             nan     0.0100   -0.0001
   760        0.7422             nan     0.0100    0.0000
   780        0.7400             nan     0.0100   -0.0001
   800        0.7371             nan     0.0100   -0.0001
   820        0.7347             nan     0.0100   -0.0000
   840        0.7326             nan     0.0100   -0.0001
   860        0.7304             nan     0.0100   -0.0000
   880        0.7281             nan     0.0100   -0.0001
   900        0.7259             nan     0.0100   -0.0001
   920        0.7239             nan     0.0100   -0.0000
   940        0.7219             nan     0.0100   -0.0001
   960        0.7197             nan     0.0100   -0.0001
   980        0.7174             nan     0.0100   -0.0002
  1000        0.7154             nan     0.0100   -0.0001
  1020        0.7135             nan     0.0100   -0.0001
  1040        0.7115             nan     0.0100   -0.0001
  1060        0.7098             nan     0.0100   -0.0001
  1080        0.7080             nan     0.0100   -0.0001
  1100        0.7060             nan     0.0100   -0.0002

- Fold02.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0039
     2        1.3167             nan     0.0100    0.0041
     3        1.3088             nan     0.0100    0.0035
     4        1.3015             nan     0.0100    0.0037
     5        1.2941             nan     0.0100    0.0034
     6        1.2866             nan     0.0100    0.0036
     7        1.2794             nan     0.0100    0.0032
     8        1.2728             nan     0.0100    0.0034
     9        1.2659             nan     0.0100    0.0033
    10        1.2598             nan     0.0100    0.0031
    20        1.2008             nan     0.0100    0.0024
    40        1.1071             nan     0.0100    0.0019
    60        1.0395             nan     0.0100    0.0014
    80        0.9877             nan     0.0100    0.0011
   100        0.9473             nan     0.0100    0.0007
   120        0.9171             nan     0.0100    0.0005
   140        0.8944             nan     0.0100    0.0003
   160        0.8757             nan     0.0100    0.0002
   180        0.8577             nan     0.0100    0.0001
   200        0.8432             nan     0.0100    0.0003
   220        0.8313             nan     0.0100    0.0001
   240        0.8202             nan     0.0100    0.0000
   260        0.8102             nan     0.0100    0.0001
   280        0.8021             nan     0.0100   -0.0001
   300        0.7945             nan     0.0100    0.0000
   320        0.7869             nan     0.0100    0.0001
   340        0.7797             nan     0.0100   -0.0000
   360        0.7730             nan     0.0100   -0.0001
   380        0.7663             nan     0.0100    0.0001
   400        0.7604             nan     0.0100   -0.0002
   420        0.7553             nan     0.0100   -0.0001
   440        0.7505             nan     0.0100   -0.0001
   460        0.7453             nan     0.0100   -0.0000
   480        0.7410             nan     0.0100   -0.0001
   500        0.7369             nan     0.0100   -0.0001
   520        0.7323             nan     0.0100   -0.0001
   540        0.7281             nan     0.0100   -0.0001
   560        0.7242             nan     0.0100   -0.0000
   580        0.7207             nan     0.0100   -0.0000
   600        0.7170             nan     0.0100   -0.0000
   620        0.7129             nan     0.0100   -0.0001
   640        0.7090             nan     0.0100    0.0000
   660        0.7051             nan     0.0100   -0.0001
   680        0.7016             nan     0.0100   -0.0001
   700        0.6982             nan     0.0100   -0.0001
   720        0.6949             nan     0.0100   -0.0001
   740        0.6915             nan     0.0100   -0.0001
   760        0.6882             nan     0.0100    0.0001
   780        0.6848             nan     0.0100   -0.0001
   800        0.6822             nan     0.0100   -0.0001
   820        0.6795             nan     0.0100   -0.0002
   840        0.6765             nan     0.0100   -0.0001
   860        0.6736             nan     0.0100   -0.0001
   880        0.6710             nan     0.0100   -0.0001
   900        0.6676             nan     0.0100   -0.0001
   920        0.6649             nan     0.0100   -0.0002
   940        0.6624             nan     0.0100   -0.0001
   960        0.6603             nan     0.0100   -0.0002
   980        0.6571             nan     0.0100   -0.0001
  1000        0.6542             nan     0.0100   -0.0001
  1020        0.6515             nan     0.0100   -0.0001
  1040        0.6488             nan     0.0100   -0.0000
  1060        0.6461             nan     0.0100   -0.0001
  1080        0.6433             nan     0.0100   -0.0002
  1100        0.6408             nan     0.0100   -0.0000

- Fold02.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2806             nan     0.1000    0.0278
     2        1.2270             nan     0.1000    0.0221
     3        1.1911             nan     0.1000    0.0173
     4        1.1624             nan     0.1000    0.0151
     5        1.1382             nan     0.1000    0.0124
     6        1.1151             nan     0.1000    0.0105
     7        1.0999             nan     0.1000    0.0073
     8        1.0833             nan     0.1000    0.0082
     9        1.0689             nan     0.1000    0.0061
    10        1.0540             nan     0.1000    0.0062
    20        0.9681             nan     0.1000    0.0025
    40        0.8885             nan     0.1000   -0.0005
    60        0.8491             nan     0.1000   -0.0001
    80        0.8278             nan     0.1000   -0.0019
   100        0.8104             nan     0.1000   -0.0008
   120        0.7983             nan     0.1000   -0.0008
   140        0.7881             nan     0.1000   -0.0001
   160        0.7799             nan     0.1000   -0.0006
   180        0.7715             nan     0.1000   -0.0005
   200        0.7656             nan     0.1000   -0.0010
   220        0.7614             nan     0.1000   -0.0013
   240        0.7567             nan     0.1000   -0.0008
   260        0.7513             nan     0.1000   -0.0004
   280        0.7468             nan     0.1000   -0.0003
   300        0.7406             nan     0.1000   -0.0004
   320        0.7367             nan     0.1000   -0.0012
   340        0.7329             nan     0.1000   -0.0005
   360        0.7292             nan     0.1000   -0.0011
   380        0.7260             nan     0.1000   -0.0002
   400        0.7223             nan     0.1000   -0.0012
   420        0.7195             nan     0.1000   -0.0002
   440        0.7158             nan     0.1000   -0.0006
   460        0.7136             nan     0.1000   -0.0009
   480        0.7095             nan     0.1000   -0.0005
   500        0.7076             nan     0.1000   -0.0009
   520        0.7050             nan     0.1000   -0.0010
   540        0.7039             nan     0.1000   -0.0009
   560        0.7016             nan     0.1000   -0.0004
   580        0.6992             nan     0.1000   -0.0002
   600        0.6965             nan     0.1000   -0.0003
   620        0.6940             nan     0.1000   -0.0012
   640        0.6929             nan     0.1000   -0.0008
   660        0.6921             nan     0.1000   -0.0013
   680        0.6890             nan     0.1000   -0.0004
   700        0.6877             nan     0.1000   -0.0009
   720        0.6859             nan     0.1000   -0.0004
   740        0.6830             nan     0.1000   -0.0004
   760        0.6826             nan     0.1000   -0.0009
   780        0.6803             nan     0.1000   -0.0005
   800        0.6782             nan     0.1000   -0.0004
   820        0.6761             nan     0.1000   -0.0008
   840        0.6747             nan     0.1000   -0.0011
   860        0.6736             nan     0.1000   -0.0005
   880        0.6737             nan     0.1000   -0.0014
   900        0.6711             nan     0.1000   -0.0012
   920        0.6685             nan     0.1000   -0.0009
   940        0.6674             nan     0.1000   -0.0005
   960        0.6651             nan     0.1000   -0.0007
   980        0.6629             nan     0.1000   -0.0002
  1000        0.6614             nan     0.1000   -0.0007
  1020        0.6605             nan     0.1000   -0.0008
  1040        0.6587             nan     0.1000   -0.0009
  1060        0.6569             nan     0.1000   -0.0012
  1080        0.6560             nan     0.1000   -0.0009
  1100        0.6541             nan     0.1000   -0.0006

- Fold02.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2721             nan     0.1000    0.0278
     2        1.2150             nan     0.1000    0.0265
     3        1.1643             nan     0.1000    0.0240
     4        1.1247             nan     0.1000    0.0192
     5        1.0911             nan     0.1000    0.0142
     6        1.0607             nan     0.1000    0.0160
     7        1.0364             nan     0.1000    0.0107
     8        1.0149             nan     0.1000    0.0107
     9        0.9956             nan     0.1000    0.0098
    10        0.9790             nan     0.1000    0.0074
    20        0.8838             nan     0.1000    0.0002
    40        0.8108             nan     0.1000   -0.0002
    60        0.7701             nan     0.1000   -0.0008
    80        0.7435             nan     0.1000   -0.0008
   100        0.7207             nan     0.1000   -0.0007
   120        0.7037             nan     0.1000   -0.0006
   140        0.6905             nan     0.1000   -0.0009
   160        0.6727             nan     0.1000   -0.0007
   180        0.6577             nan     0.1000   -0.0005
   200        0.6430             nan     0.1000   -0.0006
   220        0.6324             nan     0.1000   -0.0011
   240        0.6217             nan     0.1000   -0.0008
   260        0.6123             nan     0.1000   -0.0004
   280        0.6029             nan     0.1000   -0.0022
   300        0.5940             nan     0.1000   -0.0017
   320        0.5875             nan     0.1000   -0.0006
   340        0.5789             nan     0.1000   -0.0021
   360        0.5708             nan     0.1000   -0.0009
   380        0.5634             nan     0.1000   -0.0016
   400        0.5550             nan     0.1000   -0.0002
   420        0.5508             nan     0.1000   -0.0010
   440        0.5448             nan     0.1000   -0.0017
   460        0.5356             nan     0.1000   -0.0003
   480        0.5294             nan     0.1000   -0.0005
   500        0.5239             nan     0.1000   -0.0009
   520        0.5184             nan     0.1000   -0.0008
   540        0.5126             nan     0.1000   -0.0016
   560        0.5072             nan     0.1000   -0.0003
   580        0.5019             nan     0.1000   -0.0008
   600        0.4991             nan     0.1000   -0.0008
   620        0.4950             nan     0.1000   -0.0011
   640        0.4892             nan     0.1000   -0.0011
   660        0.4836             nan     0.1000   -0.0010
   680        0.4792             nan     0.1000   -0.0003
   700        0.4727             nan     0.1000   -0.0007
   720        0.4673             nan     0.1000   -0.0003
   740        0.4619             nan     0.1000   -0.0006
   760        0.4580             nan     0.1000   -0.0010
   780        0.4559             nan     0.1000   -0.0003
   800        0.4525             nan     0.1000   -0.0014
   820        0.4477             nan     0.1000   -0.0008
   840        0.4437             nan     0.1000   -0.0002
   860        0.4407             nan     0.1000   -0.0011
   880        0.4365             nan     0.1000   -0.0008
   900        0.4330             nan     0.1000   -0.0010
   920        0.4282             nan     0.1000   -0.0010
   940        0.4251             nan     0.1000   -0.0005
   960        0.4234             nan     0.1000   -0.0006
   980        0.4192             nan     0.1000   -0.0009
  1000        0.4163             nan     0.1000   -0.0011
  1020        0.4127             nan     0.1000   -0.0009
  1040        0.4090             nan     0.1000   -0.0002
  1060        0.4049             nan     0.1000   -0.0006
  1080        0.4020             nan     0.1000   -0.0003
  1100        0.4001             nan     0.1000   -0.0009

- Fold02.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2604             nan     0.1000    0.0403
     2        1.1932             nan     0.1000    0.0301
     3        1.1410             nan     0.1000    0.0240
     4        1.0995             nan     0.1000    0.0185
     5        1.0619             nan     0.1000    0.0172
     6        1.0349             nan     0.1000    0.0131
     7        1.0073             nan     0.1000    0.0129
     8        0.9814             nan     0.1000    0.0113
     9        0.9645             nan     0.1000    0.0059
    10        0.9460             nan     0.1000    0.0072
    20        0.8404             nan     0.1000    0.0016
    40        0.7585             nan     0.1000   -0.0008
    60        0.7122             nan     0.1000   -0.0011
    80        0.6823             nan     0.1000   -0.0009
   100        0.6559             nan     0.1000   -0.0021
   120        0.6369             nan     0.1000   -0.0015
   140        0.6149             nan     0.1000   -0.0007
   160        0.5966             nan     0.1000   -0.0016
   180        0.5776             nan     0.1000   -0.0014
   200        0.5608             nan     0.1000   -0.0021
   220        0.5467             nan     0.1000   -0.0018
   240        0.5335             nan     0.1000   -0.0016
   260        0.5221             nan     0.1000   -0.0017
   280        0.5072             nan     0.1000   -0.0003
   300        0.4989             nan     0.1000   -0.0009
   320        0.4875             nan     0.1000   -0.0013
   340        0.4796             nan     0.1000   -0.0020
   360        0.4669             nan     0.1000   -0.0015
   380        0.4561             nan     0.1000   -0.0005
   400        0.4474             nan     0.1000   -0.0005
   420        0.4401             nan     0.1000   -0.0011
   440        0.4317             nan     0.1000   -0.0015
   460        0.4231             nan     0.1000   -0.0011
   480        0.4165             nan     0.1000   -0.0008
   500        0.4095             nan     0.1000   -0.0008
   520        0.4027             nan     0.1000   -0.0009
   540        0.3963             nan     0.1000   -0.0006
   560        0.3890             nan     0.1000   -0.0009
   580        0.3833             nan     0.1000   -0.0013
   600        0.3769             nan     0.1000   -0.0010
   620        0.3701             nan     0.1000   -0.0003
   640        0.3637             nan     0.1000   -0.0006
   660        0.3561             nan     0.1000   -0.0010
   680        0.3535             nan     0.1000   -0.0010
   700        0.3491             nan     0.1000   -0.0007
   720        0.3454             nan     0.1000   -0.0010
   740        0.3396             nan     0.1000   -0.0017
   760        0.3360             nan     0.1000   -0.0010
   780        0.3318             nan     0.1000   -0.0006
   800        0.3278             nan     0.1000   -0.0009
   820        0.3235             nan     0.1000   -0.0011
   840        0.3178             nan     0.1000   -0.0008
   860        0.3125             nan     0.1000   -0.0009
   880        0.3094             nan     0.1000   -0.0007
   900        0.3057             nan     0.1000   -0.0008
   920        0.3020             nan     0.1000   -0.0009
   940        0.2979             nan     0.1000   -0.0007
   960        0.2951             nan     0.1000   -0.0014
   980        0.2892             nan     0.1000   -0.0009
  1000        0.2846             nan     0.1000   -0.0006
  1020        0.2825             nan     0.1000   -0.0012
  1040        0.2781             nan     0.1000   -0.0014
  1060        0.2729             nan     0.1000   -0.0006
  1080        0.2691             nan     0.1000   -0.0008
  1100        0.2657             nan     0.1000   -0.0008

- Fold02.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3259             nan     0.0100    0.0033
     2        1.3196             nan     0.0100    0.0033
     3        1.3130             nan     0.0100    0.0033
     4        1.3068             nan     0.0100    0.0031
     5        1.3010             nan     0.0100    0.0031
     6        1.2941             nan     0.0100    0.0030
     7        1.2885             nan     0.0100    0.0030
     8        1.2824             nan     0.0100    0.0029
     9        1.2769             nan     0.0100    0.0029
    10        1.2712             nan     0.0100    0.0028
    20        1.2192             nan     0.0100    0.0023
    40        1.1444             nan     0.0100    0.0016
    60        1.0905             nan     0.0100    0.0011
    80        1.0511             nan     0.0100    0.0008
   100        1.0195             nan     0.0100    0.0006
   120        0.9929             nan     0.0100    0.0006
   140        0.9699             nan     0.0100    0.0005
   160        0.9510             nan     0.0100    0.0003
   180        0.9350             nan     0.0100    0.0002
   200        0.9207             nan     0.0100    0.0002
   220        0.9084             nan     0.0100    0.0002
   240        0.8974             nan     0.0100    0.0002
   260        0.8877             nan     0.0100    0.0002
   280        0.8784             nan     0.0100    0.0002
   300        0.8704             nan     0.0100    0.0001
   320        0.8636             nan     0.0100    0.0001
   340        0.8565             nan     0.0100    0.0001
   360        0.8503             nan     0.0100    0.0000
   380        0.8448             nan     0.0100    0.0001
   400        0.8392             nan     0.0100    0.0000
   420        0.8340             nan     0.0100   -0.0001
   440        0.8295             nan     0.0100    0.0001
   460        0.8250             nan     0.0100    0.0000
   480        0.8210             nan     0.0100   -0.0000
   500        0.8174             nan     0.0100    0.0000
   520        0.8134             nan     0.0100   -0.0000
   540        0.8096             nan     0.0100   -0.0000
   560        0.8058             nan     0.0100   -0.0000
   580        0.8023             nan     0.0100    0.0000
   600        0.7988             nan     0.0100    0.0000
   620        0.7957             nan     0.0100   -0.0000
   640        0.7926             nan     0.0100   -0.0000
   660        0.7896             nan     0.0100    0.0000
   680        0.7868             nan     0.0100   -0.0000
   700        0.7843             nan     0.0100    0.0000
   720        0.7819             nan     0.0100   -0.0000
   740        0.7795             nan     0.0100    0.0000
   760        0.7774             nan     0.0100   -0.0000
   780        0.7751             nan     0.0100    0.0000
   800        0.7731             nan     0.0100   -0.0000
   820        0.7710             nan     0.0100   -0.0000
   840        0.7692             nan     0.0100   -0.0000
   860        0.7672             nan     0.0100   -0.0000
   880        0.7655             nan     0.0100   -0.0001
   900        0.7636             nan     0.0100   -0.0001
   920        0.7618             nan     0.0100   -0.0000
   940        0.7602             nan     0.0100   -0.0000
   960        0.7585             nan     0.0100   -0.0000
   980        0.7568             nan     0.0100   -0.0000
  1000        0.7552             nan     0.0100   -0.0000
  1020        0.7535             nan     0.0100   -0.0001
  1040        0.7520             nan     0.0100   -0.0001
  1060        0.7505             nan     0.0100   -0.0000
  1080        0.7491             nan     0.0100   -0.0000
  1100        0.7476             nan     0.0100   -0.0001

- Fold03.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0041
     2        1.3157             nan     0.0100    0.0040
     3        1.3080             nan     0.0100    0.0038
     4        1.3009             nan     0.0100    0.0038
     5        1.2936             nan     0.0100    0.0038
     6        1.2864             nan     0.0100    0.0037
     7        1.2790             nan     0.0100    0.0037
     8        1.2716             nan     0.0100    0.0036
     9        1.2644             nan     0.0100    0.0033
    10        1.2575             nan     0.0100    0.0034
    20        1.1964             nan     0.0100    0.0026
    40        1.1010             nan     0.0100    0.0020
    60        1.0316             nan     0.0100    0.0015
    80        0.9798             nan     0.0100    0.0011
   100        0.9412             nan     0.0100    0.0008
   120        0.9090             nan     0.0100    0.0006
   140        0.8854             nan     0.0100    0.0004
   160        0.8652             nan     0.0100    0.0002
   180        0.8486             nan     0.0100    0.0002
   200        0.8340             nan     0.0100    0.0002
   220        0.8214             nan     0.0100    0.0003
   240        0.8112             nan     0.0100    0.0001
   260        0.8016             nan     0.0100    0.0001
   280        0.7930             nan     0.0100    0.0001
   300        0.7857             nan     0.0100    0.0000
   320        0.7783             nan     0.0100    0.0000
   340        0.7721             nan     0.0100    0.0000
   360        0.7661             nan     0.0100    0.0000
   380        0.7601             nan     0.0100   -0.0000
   400        0.7546             nan     0.0100   -0.0002
   420        0.7496             nan     0.0100   -0.0000
   440        0.7452             nan     0.0100   -0.0001
   460        0.7406             nan     0.0100    0.0000
   480        0.7362             nan     0.0100   -0.0001
   500        0.7322             nan     0.0100    0.0000
   520        0.7280             nan     0.0100   -0.0001
   540        0.7246             nan     0.0100   -0.0001
   560        0.7214             nan     0.0100   -0.0000
   580        0.7179             nan     0.0100   -0.0001
   600        0.7147             nan     0.0100   -0.0001
   620        0.7114             nan     0.0100    0.0000
   640        0.7084             nan     0.0100   -0.0000
   660        0.7058             nan     0.0100   -0.0000
   680        0.7027             nan     0.0100   -0.0002
   700        0.7000             nan     0.0100   -0.0000
   720        0.6971             nan     0.0100   -0.0001
   740        0.6946             nan     0.0100   -0.0001
   760        0.6917             nan     0.0100   -0.0002
   780        0.6892             nan     0.0100   -0.0002
   800        0.6868             nan     0.0100   -0.0001
   820        0.6847             nan     0.0100   -0.0001
   840        0.6820             nan     0.0100   -0.0000
   860        0.6796             nan     0.0100   -0.0001
   880        0.6774             nan     0.0100   -0.0000
   900        0.6751             nan     0.0100   -0.0001
   920        0.6731             nan     0.0100   -0.0000
   940        0.6708             nan     0.0100    0.0000
   960        0.6689             nan     0.0100   -0.0001
   980        0.6668             nan     0.0100   -0.0002
  1000        0.6642             nan     0.0100   -0.0001
  1020        0.6624             nan     0.0100   -0.0000
  1040        0.6605             nan     0.0100   -0.0001
  1060        0.6585             nan     0.0100   -0.0001
  1080        0.6567             nan     0.0100   -0.0001
  1100        0.6549             nan     0.0100   -0.0001

- Fold03.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3233             nan     0.0100    0.0042
     2        1.3148             nan     0.0100    0.0041
     3        1.3061             nan     0.0100    0.0039
     4        1.2982             nan     0.0100    0.0039
     5        1.2897             nan     0.0100    0.0038
     6        1.2815             nan     0.0100    0.0039
     7        1.2734             nan     0.0100    0.0038
     8        1.2656             nan     0.0100    0.0037
     9        1.2578             nan     0.0100    0.0038
    10        1.2504             nan     0.0100    0.0035
    20        1.1829             nan     0.0100    0.0029
    40        1.0805             nan     0.0100    0.0021
    60        1.0060             nan     0.0100    0.0015
    80        0.9490             nan     0.0100    0.0010
   100        0.9052             nan     0.0100    0.0007
   120        0.8730             nan     0.0100    0.0004
   140        0.8477             nan     0.0100    0.0005
   160        0.8262             nan     0.0100    0.0002
   180        0.8090             nan     0.0100    0.0002
   200        0.7938             nan     0.0100    0.0002
   220        0.7807             nan     0.0100    0.0001
   240        0.7698             nan     0.0100    0.0001
   260        0.7595             nan     0.0100    0.0000
   280        0.7506             nan     0.0100   -0.0000
   300        0.7421             nan     0.0100    0.0000
   320        0.7343             nan     0.0100   -0.0001
   340        0.7277             nan     0.0100    0.0001
   360        0.7206             nan     0.0100   -0.0001
   380        0.7147             nan     0.0100   -0.0001
   400        0.7092             nan     0.0100   -0.0001
   420        0.7046             nan     0.0100   -0.0001
   440        0.6995             nan     0.0100   -0.0002
   460        0.6940             nan     0.0100    0.0000
   480        0.6896             nan     0.0100   -0.0001
   500        0.6846             nan     0.0100   -0.0001
   520        0.6802             nan     0.0100   -0.0001
   540        0.6761             nan     0.0100   -0.0000
   560        0.6718             nan     0.0100   -0.0002
   580        0.6683             nan     0.0100   -0.0002
   600        0.6644             nan     0.0100   -0.0001
   620        0.6608             nan     0.0100   -0.0000
   640        0.6574             nan     0.0100   -0.0001
   660        0.6543             nan     0.0100   -0.0001
   680        0.6507             nan     0.0100   -0.0001
   700        0.6477             nan     0.0100   -0.0000
   720        0.6451             nan     0.0100   -0.0002
   740        0.6419             nan     0.0100   -0.0000
   760        0.6391             nan     0.0100   -0.0001
   780        0.6363             nan     0.0100   -0.0001
   800        0.6333             nan     0.0100   -0.0002
   820        0.6306             nan     0.0100   -0.0001
   840        0.6281             nan     0.0100   -0.0001
   860        0.6253             nan     0.0100    0.0000
   880        0.6228             nan     0.0100   -0.0001
   900        0.6202             nan     0.0100   -0.0003
   920        0.6174             nan     0.0100   -0.0002
   940        0.6150             nan     0.0100   -0.0001
   960        0.6122             nan     0.0100   -0.0002
   980        0.6093             nan     0.0100   -0.0001
  1000        0.6072             nan     0.0100   -0.0001
  1020        0.6050             nan     0.0100   -0.0000
  1040        0.6025             nan     0.0100   -0.0001
  1060        0.6006             nan     0.0100   -0.0001
  1080        0.5982             nan     0.0100   -0.0001
  1100        0.5962             nan     0.0100   -0.0001

- Fold03.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2656             nan     0.1000    0.0318
     2        1.2097             nan     0.1000    0.0255
     3        1.1668             nan     0.1000    0.0203
     4        1.1322             nan     0.1000    0.0164
     5        1.1061             nan     0.1000    0.0135
     6        1.0796             nan     0.1000    0.0109
     7        1.0587             nan     0.1000    0.0092
     8        1.0414             nan     0.1000    0.0075
     9        1.0241             nan     0.1000    0.0079
    10        1.0081             nan     0.1000    0.0062
    20        0.9136             nan     0.1000    0.0029
    40        0.8359             nan     0.1000   -0.0001
    60        0.7971             nan     0.1000   -0.0003
    80        0.7710             nan     0.1000   -0.0003
   100        0.7550             nan     0.1000   -0.0001
   120        0.7400             nan     0.1000   -0.0008
   140        0.7304             nan     0.1000   -0.0009
   160        0.7227             nan     0.1000   -0.0009
   180        0.7149             nan     0.1000   -0.0004
   200        0.7079             nan     0.1000   -0.0009
   220        0.7014             nan     0.1000   -0.0009
   240        0.6952             nan     0.1000   -0.0010
   260        0.6908             nan     0.1000   -0.0001
   280        0.6864             nan     0.1000   -0.0007
   300        0.6827             nan     0.1000   -0.0005
   320        0.6783             nan     0.1000   -0.0002
   340        0.6741             nan     0.1000   -0.0005
   360        0.6706             nan     0.1000   -0.0010
   380        0.6687             nan     0.1000   -0.0010
   400        0.6650             nan     0.1000   -0.0008
   420        0.6617             nan     0.1000   -0.0003
   440        0.6590             nan     0.1000   -0.0006
   460        0.6560             nan     0.1000   -0.0009
   480        0.6532             nan     0.1000   -0.0010
   500        0.6509             nan     0.1000   -0.0008
   520        0.6484             nan     0.1000   -0.0015
   540        0.6467             nan     0.1000   -0.0005
   560        0.6444             nan     0.1000   -0.0005
   580        0.6433             nan     0.1000   -0.0005
   600        0.6416             nan     0.1000   -0.0003
   620        0.6393             nan     0.1000   -0.0006
   640        0.6377             nan     0.1000   -0.0006
   660        0.6354             nan     0.1000   -0.0005
   680        0.6347             nan     0.1000   -0.0015
   700        0.6315             nan     0.1000   -0.0008
   720        0.6300             nan     0.1000   -0.0009
   740        0.6285             nan     0.1000   -0.0004
   760        0.6267             nan     0.1000   -0.0008
   780        0.6250             nan     0.1000   -0.0013
   800        0.6240             nan     0.1000   -0.0008
   820        0.6227             nan     0.1000   -0.0013
   840        0.6221             nan     0.1000   -0.0003
   860        0.6203             nan     0.1000   -0.0003
   880        0.6185             nan     0.1000   -0.0007
   900        0.6163             nan     0.1000   -0.0006
   920        0.6155             nan     0.1000   -0.0006
   940        0.6146             nan     0.1000   -0.0004
   960        0.6130             nan     0.1000   -0.0010
   980        0.6105             nan     0.1000   -0.0026
  1000        0.6095             nan     0.1000   -0.0003
  1020        0.6071             nan     0.1000   -0.0005
  1040        0.6069             nan     0.1000   -0.0009
  1060        0.6061             nan     0.1000   -0.0005
  1080        0.6049             nan     0.1000   -0.0009
  1100        0.6036             nan     0.1000   -0.0008

- Fold03.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2527             nan     0.1000    0.0364
     2        1.1924             nan     0.1000    0.0273
     3        1.1384             nan     0.1000    0.0262
     4        1.0938             nan     0.1000    0.0223
     5        1.0580             nan     0.1000    0.0164
     6        1.0250             nan     0.1000    0.0140
     7        0.9986             nan     0.1000    0.0131
     8        0.9745             nan     0.1000    0.0108
     9        0.9549             nan     0.1000    0.0086
    10        0.9376             nan     0.1000    0.0093
    20        0.8343             nan     0.1000    0.0020
    40        0.7623             nan     0.1000    0.0002
    60        0.7218             nan     0.1000   -0.0000
    80        0.6951             nan     0.1000   -0.0005
   100        0.6746             nan     0.1000   -0.0010
   120        0.6558             nan     0.1000   -0.0009
   140        0.6381             nan     0.1000   -0.0007
   160        0.6203             nan     0.1000   -0.0006
   180        0.6039             nan     0.1000   -0.0005
   200        0.5935             nan     0.1000   -0.0013
   220        0.5831             nan     0.1000   -0.0014
   240        0.5722             nan     0.1000   -0.0008
   260        0.5627             nan     0.1000   -0.0010
   280        0.5535             nan     0.1000   -0.0011
   300        0.5455             nan     0.1000   -0.0009
   320        0.5369             nan     0.1000   -0.0010
   340        0.5281             nan     0.1000   -0.0010
   360        0.5203             nan     0.1000   -0.0004
   380        0.5152             nan     0.1000   -0.0007
   400        0.5038             nan     0.1000   -0.0010
   420        0.4992             nan     0.1000   -0.0009
   440        0.4895             nan     0.1000   -0.0002
   460        0.4857             nan     0.1000   -0.0008
   480        0.4795             nan     0.1000   -0.0005
   500        0.4728             nan     0.1000   -0.0013
   520        0.4651             nan     0.1000   -0.0002
   540        0.4584             nan     0.1000   -0.0009
   560        0.4525             nan     0.1000   -0.0013
   580        0.4474             nan     0.1000   -0.0016
   600        0.4444             nan     0.1000   -0.0007
   620        0.4393             nan     0.1000   -0.0006
   640        0.4349             nan     0.1000   -0.0010
   660        0.4307             nan     0.1000   -0.0007
   680        0.4261             nan     0.1000   -0.0006
   700        0.4204             nan     0.1000   -0.0002
   720        0.4146             nan     0.1000   -0.0015
   740        0.4112             nan     0.1000   -0.0006
   760        0.4073             nan     0.1000   -0.0006
   780        0.4014             nan     0.1000   -0.0002
   800        0.3981             nan     0.1000   -0.0018
   820        0.3945             nan     0.1000   -0.0002
   840        0.3926             nan     0.1000   -0.0012
   860        0.3896             nan     0.1000   -0.0011
   880        0.3858             nan     0.1000   -0.0004
   900        0.3813             nan     0.1000   -0.0005
   920        0.3779             nan     0.1000   -0.0009
   940        0.3738             nan     0.1000   -0.0011
   960        0.3692             nan     0.1000   -0.0010
   980        0.3661             nan     0.1000   -0.0007
  1000        0.3622             nan     0.1000   -0.0003
  1020        0.3590             nan     0.1000   -0.0011
  1040        0.3551             nan     0.1000   -0.0010
  1060        0.3523             nan     0.1000   -0.0008
  1080        0.3495             nan     0.1000   -0.0008
  1100        0.3462             nan     0.1000   -0.0006

- Fold03.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2489             nan     0.1000    0.0413
     2        1.1828             nan     0.1000    0.0333
     3        1.1255             nan     0.1000    0.0280
     4        1.0775             nan     0.1000    0.0234
     5        1.0344             nan     0.1000    0.0197
     6        1.0028             nan     0.1000    0.0159
     7        0.9728             nan     0.1000    0.0141
     8        0.9469             nan     0.1000    0.0104
     9        0.9232             nan     0.1000    0.0102
    10        0.9055             nan     0.1000    0.0077
    20        0.7933             nan     0.1000    0.0008
    40        0.7114             nan     0.1000   -0.0005
    60        0.6677             nan     0.1000   -0.0009
    80        0.6346             nan     0.1000   -0.0011
   100        0.6109             nan     0.1000   -0.0008
   120        0.5895             nan     0.1000   -0.0006
   140        0.5658             nan     0.1000   -0.0020
   160        0.5466             nan     0.1000   -0.0015
   180        0.5271             nan     0.1000   -0.0006
   200        0.5116             nan     0.1000   -0.0014
   220        0.5014             nan     0.1000   -0.0017
   240        0.4862             nan     0.1000   -0.0007
   260        0.4716             nan     0.1000   -0.0005
   280        0.4553             nan     0.1000   -0.0005
   300        0.4435             nan     0.1000   -0.0013
   320        0.4334             nan     0.1000   -0.0007
   340        0.4254             nan     0.1000   -0.0006
   360        0.4164             nan     0.1000   -0.0009
   380        0.4086             nan     0.1000   -0.0010
   400        0.4021             nan     0.1000   -0.0010
   420        0.3956             nan     0.1000   -0.0014
   440        0.3879             nan     0.1000   -0.0009
   460        0.3808             nan     0.1000   -0.0007
   480        0.3726             nan     0.1000   -0.0008
   500        0.3671             nan     0.1000   -0.0013
   520        0.3614             nan     0.1000   -0.0008
   540        0.3548             nan     0.1000   -0.0008
   560        0.3495             nan     0.1000   -0.0009
   580        0.3427             nan     0.1000   -0.0014
   600        0.3356             nan     0.1000   -0.0015
   620        0.3299             nan     0.1000   -0.0005
   640        0.3253             nan     0.1000   -0.0008
   660        0.3190             nan     0.1000   -0.0008
   680        0.3130             nan     0.1000   -0.0009
   700        0.3069             nan     0.1000   -0.0008
   720        0.3022             nan     0.1000   -0.0010
   740        0.2969             nan     0.1000   -0.0010
   760        0.2913             nan     0.1000   -0.0012
   780        0.2859             nan     0.1000   -0.0013
   800        0.2823             nan     0.1000   -0.0017
   820        0.2774             nan     0.1000   -0.0006
   840        0.2726             nan     0.1000   -0.0006
   860        0.2698             nan     0.1000   -0.0007
   880        0.2661             nan     0.1000   -0.0004
   900        0.2609             nan     0.1000   -0.0006
   920        0.2566             nan     0.1000   -0.0007
   940        0.2529             nan     0.1000   -0.0009
   960        0.2503             nan     0.1000   -0.0011
   980        0.2466             nan     0.1000   -0.0009
  1000        0.2427             nan     0.1000   -0.0006
  1020        0.2399             nan     0.1000   -0.0002
  1040        0.2372             nan     0.1000   -0.0010
  1060        0.2340             nan     0.1000   -0.0006
  1080        0.2316             nan     0.1000   -0.0011
  1100        0.2283             nan     0.1000   -0.0006

- Fold03.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0030
     2        1.3199             nan     0.0100    0.0029
     3        1.3142             nan     0.0100    0.0028
     4        1.3086             nan     0.0100    0.0028
     5        1.3028             nan     0.0100    0.0027
     6        1.2978             nan     0.0100    0.0027
     7        1.2927             nan     0.0100    0.0027
     8        1.2873             nan     0.0100    0.0026
     9        1.2816             nan     0.0100    0.0025
    10        1.2769             nan     0.0100    0.0025
    20        1.2322             nan     0.0100    0.0021
    40        1.1615             nan     0.0100    0.0014
    60        1.1119             nan     0.0100    0.0010
    80        1.0753             nan     0.0100    0.0008
   100        1.0442             nan     0.0100    0.0003
   120        1.0193             nan     0.0100    0.0003
   140        0.9973             nan     0.0100    0.0004
   160        0.9784             nan     0.0100    0.0004
   180        0.9629             nan     0.0100    0.0003
   200        0.9488             nan     0.0100    0.0003
   220        0.9367             nan     0.0100    0.0002
   240        0.9253             nan     0.0100    0.0002
   260        0.9153             nan     0.0100    0.0002
   280        0.9061             nan     0.0100    0.0001
   300        0.8980             nan     0.0100    0.0001
   320        0.8905             nan     0.0100    0.0001
   340        0.8838             nan     0.0100    0.0001
   360        0.8774             nan     0.0100    0.0001
   380        0.8721             nan     0.0100    0.0001
   400        0.8669             nan     0.0100   -0.0001
   420        0.8618             nan     0.0100    0.0000
   440        0.8580             nan     0.0100    0.0000
   460        0.8536             nan     0.0100    0.0000
   480        0.8496             nan     0.0100    0.0000
   500        0.8456             nan     0.0100   -0.0000
   520        0.8418             nan     0.0100   -0.0000
   540        0.8379             nan     0.0100   -0.0000
   560        0.8344             nan     0.0100    0.0000
   580        0.8312             nan     0.0100    0.0001
   600        0.8281             nan     0.0100   -0.0000
   620        0.8252             nan     0.0100    0.0000
   640        0.8222             nan     0.0100   -0.0002
   660        0.8194             nan     0.0100    0.0000
   680        0.8169             nan     0.0100   -0.0000
   700        0.8140             nan     0.0100    0.0000
   720        0.8114             nan     0.0100   -0.0000
   740        0.8090             nan     0.0100   -0.0000
   760        0.8066             nan     0.0100   -0.0000
   780        0.8042             nan     0.0100    0.0000
   800        0.8022             nan     0.0100   -0.0001
   820        0.8001             nan     0.0100   -0.0001
   840        0.7981             nan     0.0100    0.0000
   860        0.7961             nan     0.0100   -0.0001
   880        0.7943             nan     0.0100   -0.0000
   900        0.7925             nan     0.0100   -0.0000
   920        0.7904             nan     0.0100   -0.0000
   940        0.7884             nan     0.0100   -0.0000
   960        0.7866             nan     0.0100   -0.0001
   980        0.7850             nan     0.0100   -0.0001
  1000        0.7831             nan     0.0100   -0.0000
  1020        0.7815             nan     0.0100   -0.0000
  1040        0.7799             nan     0.0100   -0.0000
  1060        0.7783             nan     0.0100   -0.0001
  1080        0.7767             nan     0.0100   -0.0002
  1100        0.7752             nan     0.0100   -0.0001

- Fold04.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3247             nan     0.0100    0.0037
     2        1.3173             nan     0.0100    0.0036
     3        1.3100             nan     0.0100    0.0034
     4        1.3028             nan     0.0100    0.0035
     5        1.2955             nan     0.0100    0.0034
     6        1.2885             nan     0.0100    0.0034
     7        1.2816             nan     0.0100    0.0032
     8        1.2756             nan     0.0100    0.0028
     9        1.2692             nan     0.0100    0.0030
    10        1.2628             nan     0.0100    0.0031
    20        1.2041             nan     0.0100    0.0026
    40        1.1156             nan     0.0100    0.0017
    60        1.0503             nan     0.0100    0.0013
    80        1.0005             nan     0.0100    0.0010
   100        0.9623             nan     0.0100    0.0008
   120        0.9337             nan     0.0100    0.0005
   140        0.9103             nan     0.0100    0.0004
   160        0.8913             nan     0.0100    0.0004
   180        0.8751             nan     0.0100    0.0002
   200        0.8603             nan     0.0100    0.0002
   220        0.8490             nan     0.0100    0.0003
   240        0.8383             nan     0.0100    0.0001
   260        0.8278             nan     0.0100    0.0000
   280        0.8194             nan     0.0100    0.0000
   300        0.8122             nan     0.0100    0.0001
   320        0.8049             nan     0.0100    0.0000
   340        0.7985             nan     0.0100   -0.0000
   360        0.7932             nan     0.0100   -0.0000
   380        0.7878             nan     0.0100    0.0000
   400        0.7825             nan     0.0100    0.0000
   420        0.7771             nan     0.0100    0.0000
   440        0.7719             nan     0.0100   -0.0000
   460        0.7676             nan     0.0100   -0.0000
   480        0.7634             nan     0.0100    0.0000
   500        0.7593             nan     0.0100    0.0000
   520        0.7555             nan     0.0100   -0.0001
   540        0.7519             nan     0.0100   -0.0000
   560        0.7488             nan     0.0100   -0.0000
   580        0.7455             nan     0.0100   -0.0001
   600        0.7420             nan     0.0100   -0.0002
   620        0.7386             nan     0.0100   -0.0000
   640        0.7356             nan     0.0100   -0.0001
   660        0.7326             nan     0.0100   -0.0001
   680        0.7298             nan     0.0100   -0.0001
   700        0.7269             nan     0.0100    0.0000
   720        0.7246             nan     0.0100   -0.0001
   740        0.7220             nan     0.0100   -0.0001
   760        0.7190             nan     0.0100   -0.0001
   780        0.7167             nan     0.0100   -0.0001
   800        0.7139             nan     0.0100   -0.0000
   820        0.7114             nan     0.0100   -0.0000
   840        0.7087             nan     0.0100   -0.0000
   860        0.7065             nan     0.0100   -0.0000
   880        0.7042             nan     0.0100   -0.0001
   900        0.7018             nan     0.0100   -0.0001
   920        0.7001             nan     0.0100   -0.0002
   940        0.6979             nan     0.0100   -0.0001
   960        0.6958             nan     0.0100   -0.0000
   980        0.6938             nan     0.0100   -0.0001
  1000        0.6919             nan     0.0100   -0.0001
  1020        0.6900             nan     0.0100   -0.0001
  1040        0.6876             nan     0.0100   -0.0000
  1060        0.6858             nan     0.0100   -0.0001
  1080        0.6836             nan     0.0100   -0.0001
  1100        0.6816             nan     0.0100   -0.0001

- Fold04.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3238             nan     0.0100    0.0043
     2        1.3157             nan     0.0100    0.0039
     3        1.3077             nan     0.0100    0.0040
     4        1.3002             nan     0.0100    0.0039
     5        1.2925             nan     0.0100    0.0036
     6        1.2848             nan     0.0100    0.0038
     7        1.2776             nan     0.0100    0.0036
     8        1.2701             nan     0.0100    0.0035
     9        1.2632             nan     0.0100    0.0032
    10        1.2563             nan     0.0100    0.0033
    20        1.1918             nan     0.0100    0.0027
    40        1.0936             nan     0.0100    0.0019
    60        1.0215             nan     0.0100    0.0013
    80        0.9682             nan     0.0100    0.0011
   100        0.9273             nan     0.0100    0.0007
   120        0.8956             nan     0.0100    0.0005
   140        0.8725             nan     0.0100    0.0003
   160        0.8528             nan     0.0100    0.0002
   180        0.8363             nan     0.0100    0.0002
   200        0.8216             nan     0.0100    0.0000
   220        0.8099             nan     0.0100   -0.0001
   240        0.7988             nan     0.0100   -0.0001
   260        0.7894             nan     0.0100   -0.0001
   280        0.7815             nan     0.0100   -0.0001
   300        0.7733             nan     0.0100    0.0001
   320        0.7658             nan     0.0100    0.0000
   340        0.7593             nan     0.0100   -0.0000
   360        0.7530             nan     0.0100    0.0000
   380        0.7470             nan     0.0100   -0.0000
   400        0.7416             nan     0.0100   -0.0001
   420        0.7358             nan     0.0100   -0.0001
   440        0.7304             nan     0.0100   -0.0000
   460        0.7259             nan     0.0100   -0.0001
   480        0.7205             nan     0.0100   -0.0001
   500        0.7156             nan     0.0100   -0.0001
   520        0.7116             nan     0.0100   -0.0001
   540        0.7074             nan     0.0100   -0.0001
   560        0.7041             nan     0.0100   -0.0001
   580        0.7006             nan     0.0100   -0.0001
   600        0.6962             nan     0.0100   -0.0001
   620        0.6927             nan     0.0100   -0.0002
   640        0.6888             nan     0.0100   -0.0001
   660        0.6858             nan     0.0100   -0.0002
   680        0.6824             nan     0.0100   -0.0001
   700        0.6786             nan     0.0100    0.0000
   720        0.6755             nan     0.0100   -0.0001
   740        0.6719             nan     0.0100   -0.0001
   760        0.6687             nan     0.0100   -0.0001
   780        0.6657             nan     0.0100   -0.0000
   800        0.6627             nan     0.0100   -0.0000
   820        0.6596             nan     0.0100   -0.0001
   840        0.6567             nan     0.0100   -0.0001
   860        0.6530             nan     0.0100   -0.0001
   880        0.6503             nan     0.0100   -0.0001
   900        0.6474             nan     0.0100   -0.0000
   920        0.6450             nan     0.0100   -0.0001
   940        0.6425             nan     0.0100   -0.0002
   960        0.6394             nan     0.0100   -0.0001
   980        0.6370             nan     0.0100   -0.0000
  1000        0.6345             nan     0.0100   -0.0000
  1020        0.6318             nan     0.0100   -0.0001
  1040        0.6294             nan     0.0100   -0.0001
  1060        0.6268             nan     0.0100   -0.0000
  1080        0.6242             nan     0.0100   -0.0001
  1100        0.6216             nan     0.0100   -0.0001

- Fold04.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2725             nan     0.1000    0.0284
     2        1.2293             nan     0.1000    0.0228
     3        1.1911             nan     0.1000    0.0194
     4        1.1588             nan     0.1000    0.0159
     5        1.1294             nan     0.1000    0.0127
     6        1.1093             nan     0.1000    0.0083
     7        1.0895             nan     0.1000    0.0101
     8        1.0705             nan     0.1000    0.0084
     9        1.0540             nan     0.1000    0.0074
    10        1.0383             nan     0.1000    0.0071
    20        0.9426             nan     0.1000    0.0020
    40        0.8649             nan     0.1000    0.0001
    60        0.8269             nan     0.1000   -0.0003
    80        0.8018             nan     0.1000    0.0004
   100        0.7848             nan     0.1000    0.0000
   120        0.7676             nan     0.1000   -0.0001
   140        0.7556             nan     0.1000   -0.0003
   160        0.7473             nan     0.1000   -0.0008
   180        0.7387             nan     0.1000   -0.0005
   200        0.7327             nan     0.1000   -0.0013
   220        0.7253             nan     0.1000   -0.0003
   240        0.7203             nan     0.1000   -0.0008
   260        0.7141             nan     0.1000   -0.0006
   280        0.7106             nan     0.1000   -0.0007
   300        0.7049             nan     0.1000   -0.0006
   320        0.6993             nan     0.1000   -0.0009
   340        0.6956             nan     0.1000   -0.0009
   360        0.6936             nan     0.1000   -0.0007
   380        0.6883             nan     0.1000   -0.0004
   400        0.6851             nan     0.1000   -0.0006
   420        0.6823             nan     0.1000   -0.0010
   440        0.6783             nan     0.1000   -0.0005
   460        0.6758             nan     0.1000   -0.0009
   480        0.6734             nan     0.1000   -0.0005
   500        0.6715             nan     0.1000   -0.0010
   520        0.6682             nan     0.1000   -0.0007
   540        0.6663             nan     0.1000   -0.0012
   560        0.6638             nan     0.1000   -0.0007
   580        0.6622             nan     0.1000   -0.0010
   600        0.6598             nan     0.1000   -0.0004
   620        0.6583             nan     0.1000   -0.0011
   640        0.6549             nan     0.1000    0.0001
   660        0.6527             nan     0.1000   -0.0006
   680        0.6510             nan     0.1000   -0.0017
   700        0.6486             nan     0.1000   -0.0006
   720        0.6470             nan     0.1000   -0.0008
   740        0.6455             nan     0.1000   -0.0015
   760        0.6435             nan     0.1000   -0.0009
   780        0.6424             nan     0.1000   -0.0007
   800        0.6416             nan     0.1000   -0.0006
   820        0.6404             nan     0.1000   -0.0008
   840        0.6390             nan     0.1000   -0.0003
   860        0.6379             nan     0.1000   -0.0004
   880        0.6359             nan     0.1000   -0.0013
   900        0.6347             nan     0.1000   -0.0002
   920        0.6326             nan     0.1000   -0.0014
   940        0.6306             nan     0.1000   -0.0005
   960        0.6288             nan     0.1000   -0.0010
   980        0.6278             nan     0.1000   -0.0009
  1000        0.6264             nan     0.1000   -0.0011
  1020        0.6249             nan     0.1000   -0.0011
  1040        0.6237             nan     0.1000   -0.0004
  1060        0.6219             nan     0.1000   -0.0011
  1080        0.6211             nan     0.1000   -0.0012
  1100        0.6205             nan     0.1000   -0.0010

- Fold04.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2617             nan     0.1000    0.0355
     2        1.2069             nan     0.1000    0.0276
     3        1.1577             nan     0.1000    0.0234
     4        1.1136             nan     0.1000    0.0214
     5        1.0797             nan     0.1000    0.0173
     6        1.0487             nan     0.1000    0.0153
     7        1.0216             nan     0.1000    0.0127
     8        0.9972             nan     0.1000    0.0105
     9        0.9763             nan     0.1000    0.0078
    10        0.9583             nan     0.1000    0.0073
    20        0.8632             nan     0.1000    0.0004
    40        0.7901             nan     0.1000    0.0001
    60        0.7519             nan     0.1000    0.0001
    80        0.7230             nan     0.1000   -0.0007
   100        0.7031             nan     0.1000   -0.0007
   120        0.6815             nan     0.1000   -0.0009
   140        0.6652             nan     0.1000   -0.0007
   160        0.6467             nan     0.1000   -0.0001
   180        0.6343             nan     0.1000   -0.0014
   200        0.6179             nan     0.1000   -0.0012
   220        0.6062             nan     0.1000   -0.0005
   240        0.5929             nan     0.1000   -0.0005
   260        0.5824             nan     0.1000   -0.0008
   280        0.5714             nan     0.1000   -0.0002
   300        0.5636             nan     0.1000   -0.0008
   320        0.5541             nan     0.1000   -0.0007
   340        0.5442             nan     0.1000   -0.0004
   360        0.5376             nan     0.1000   -0.0009
   380        0.5286             nan     0.1000   -0.0012
   400        0.5217             nan     0.1000   -0.0009
   420        0.5157             nan     0.1000   -0.0013
   440        0.5078             nan     0.1000   -0.0008
   460        0.5023             nan     0.1000   -0.0005
   480        0.4975             nan     0.1000   -0.0004
   500        0.4911             nan     0.1000   -0.0009
   520        0.4849             nan     0.1000   -0.0009
   540        0.4788             nan     0.1000   -0.0007
   560        0.4744             nan     0.1000   -0.0008
   580        0.4678             nan     0.1000   -0.0010
   600        0.4660             nan     0.1000   -0.0007
   620        0.4621             nan     0.1000   -0.0008
   640        0.4552             nan     0.1000   -0.0006
   660        0.4499             nan     0.1000   -0.0010
   680        0.4457             nan     0.1000   -0.0008
   700        0.4404             nan     0.1000   -0.0008
   720        0.4366             nan     0.1000   -0.0011
   740        0.4315             nan     0.1000   -0.0010
   760        0.4267             nan     0.1000   -0.0010
   780        0.4223             nan     0.1000   -0.0006
   800        0.4192             nan     0.1000   -0.0005
   820        0.4128             nan     0.1000   -0.0007
   840        0.4095             nan     0.1000   -0.0012
   860        0.4059             nan     0.1000   -0.0007
   880        0.4019             nan     0.1000   -0.0006
   900        0.3977             nan     0.1000   -0.0006
   920        0.3945             nan     0.1000   -0.0005
   940        0.3903             nan     0.1000   -0.0007
   960        0.3877             nan     0.1000   -0.0007
   980        0.3845             nan     0.1000   -0.0012
  1000        0.3807             nan     0.1000   -0.0004
  1020        0.3773             nan     0.1000   -0.0006
  1040        0.3751             nan     0.1000   -0.0009
  1060        0.3713             nan     0.1000   -0.0007
  1080        0.3695             nan     0.1000   -0.0003
  1100        0.3661             nan     0.1000   -0.0008

- Fold04.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2523             nan     0.1000    0.0385
     2        1.1856             nan     0.1000    0.0334
     3        1.1299             nan     0.1000    0.0260
     4        1.0882             nan     0.1000    0.0175
     5        1.0480             nan     0.1000    0.0196
     6        1.0168             nan     0.1000    0.0130
     7        0.9879             nan     0.1000    0.0133
     8        0.9596             nan     0.1000    0.0117
     9        0.9381             nan     0.1000    0.0090
    10        0.9230             nan     0.1000    0.0059
    20        0.8198             nan     0.1000    0.0009
    40        0.7392             nan     0.1000   -0.0000
    60        0.6967             nan     0.1000   -0.0010
    80        0.6633             nan     0.1000   -0.0016
   100        0.6375             nan     0.1000   -0.0004
   120        0.6155             nan     0.1000   -0.0014
   140        0.5953             nan     0.1000   -0.0016
   160        0.5782             nan     0.1000   -0.0024
   180        0.5579             nan     0.1000   -0.0014
   200        0.5431             nan     0.1000   -0.0010
   220        0.5259             nan     0.1000   -0.0005
   240        0.5142             nan     0.1000   -0.0026
   260        0.5024             nan     0.1000   -0.0010
   280        0.4902             nan     0.1000   -0.0007
   300        0.4796             nan     0.1000   -0.0006
   320        0.4668             nan     0.1000   -0.0008
   340        0.4573             nan     0.1000   -0.0008
   360        0.4453             nan     0.1000   -0.0013
   380        0.4382             nan     0.1000   -0.0014
   400        0.4272             nan     0.1000   -0.0010
   420        0.4160             nan     0.1000   -0.0011
   440        0.4081             nan     0.1000   -0.0009
   460        0.4003             nan     0.1000   -0.0004
   480        0.3927             nan     0.1000   -0.0014
   500        0.3835             nan     0.1000   -0.0010
   520        0.3761             nan     0.1000   -0.0008
   540        0.3716             nan     0.1000   -0.0008
   560        0.3681             nan     0.1000   -0.0014
   580        0.3626             nan     0.1000   -0.0016
   600        0.3552             nan     0.1000   -0.0017
   620        0.3479             nan     0.1000   -0.0007
   640        0.3420             nan     0.1000   -0.0009
   660        0.3360             nan     0.1000   -0.0007
   680        0.3305             nan     0.1000   -0.0015
   700        0.3234             nan     0.1000   -0.0011
   720        0.3179             nan     0.1000   -0.0011
   740        0.3127             nan     0.1000   -0.0010
   760        0.3072             nan     0.1000   -0.0007
   780        0.3029             nan     0.1000   -0.0013
   800        0.2999             nan     0.1000   -0.0002
   820        0.2944             nan     0.1000   -0.0009
   840        0.2904             nan     0.1000   -0.0007
   860        0.2879             nan     0.1000   -0.0008
   880        0.2853             nan     0.1000   -0.0015
   900        0.2820             nan     0.1000   -0.0008
   920        0.2781             nan     0.1000   -0.0012
   940        0.2745             nan     0.1000   -0.0010
   960        0.2699             nan     0.1000   -0.0005
   980        0.2659             nan     0.1000   -0.0013
  1000        0.2624             nan     0.1000   -0.0008
  1020        0.2578             nan     0.1000   -0.0010
  1040        0.2546             nan     0.1000   -0.0007
  1060        0.2500             nan     0.1000   -0.0004
  1080        0.2468             nan     0.1000   -0.0005
  1100        0.2435             nan     0.1000   -0.0009

- Fold04.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3263             nan     0.0100    0.0030
     2        1.3203             nan     0.0100    0.0029
     3        1.3142             nan     0.0100    0.0029
     4        1.3084             nan     0.0100    0.0028
     5        1.3031             nan     0.0100    0.0028
     6        1.2973             nan     0.0100    0.0027
     7        1.2918             nan     0.0100    0.0028
     8        1.2866             nan     0.0100    0.0027
     9        1.2817             nan     0.0100    0.0026
    10        1.2768             nan     0.0100    0.0025
    20        1.2323             nan     0.0100    0.0021
    40        1.1618             nan     0.0100    0.0015
    60        1.1105             nan     0.0100    0.0010
    80        1.0741             nan     0.0100    0.0008
   100        1.0458             nan     0.0100    0.0006
   120        1.0214             nan     0.0100    0.0005
   140        1.0006             nan     0.0100    0.0004
   160        0.9834             nan     0.0100    0.0003
   180        0.9695             nan     0.0100    0.0003
   200        0.9574             nan     0.0100    0.0003
   220        0.9464             nan     0.0100    0.0002
   240        0.9363             nan     0.0100    0.0001
   260        0.9279             nan     0.0100    0.0002
   280        0.9193             nan     0.0100    0.0002
   300        0.9121             nan     0.0100    0.0001
   320        0.9051             nan     0.0100    0.0000
   340        0.8990             nan     0.0100    0.0001
   360        0.8936             nan     0.0100    0.0001
   380        0.8880             nan     0.0100   -0.0001
   400        0.8827             nan     0.0100   -0.0000
   420        0.8779             nan     0.0100   -0.0000
   440        0.8731             nan     0.0100    0.0001
   460        0.8687             nan     0.0100    0.0001
   480        0.8649             nan     0.0100    0.0000
   500        0.8611             nan     0.0100   -0.0000
   520        0.8572             nan     0.0100   -0.0000
   540        0.8536             nan     0.0100   -0.0001
   560        0.8502             nan     0.0100   -0.0000
   580        0.8471             nan     0.0100   -0.0000
   600        0.8440             nan     0.0100   -0.0000
   620        0.8411             nan     0.0100   -0.0000
   640        0.8383             nan     0.0100   -0.0000
   660        0.8356             nan     0.0100   -0.0000
   680        0.8332             nan     0.0100   -0.0000
   700        0.8308             nan     0.0100    0.0000
   720        0.8284             nan     0.0100   -0.0001
   740        0.8262             nan     0.0100   -0.0000
   760        0.8239             nan     0.0100   -0.0000
   780        0.8215             nan     0.0100   -0.0000
   800        0.8195             nan     0.0100   -0.0000
   820        0.8174             nan     0.0100   -0.0000
   840        0.8155             nan     0.0100   -0.0000
   860        0.8136             nan     0.0100   -0.0002
   880        0.8119             nan     0.0100    0.0000
   900        0.8101             nan     0.0100   -0.0001
   920        0.8084             nan     0.0100   -0.0000
   940        0.8068             nan     0.0100   -0.0002
   960        0.8053             nan     0.0100   -0.0001
   980        0.8039             nan     0.0100   -0.0001
  1000        0.8024             nan     0.0100   -0.0000
  1020        0.8009             nan     0.0100   -0.0002
  1040        0.7995             nan     0.0100   -0.0000
  1060        0.7982             nan     0.0100   -0.0001
  1080        0.7968             nan     0.0100   -0.0001
  1100        0.7955             nan     0.0100   -0.0002

- Fold05.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0036
     2        1.3170             nan     0.0100    0.0036
     3        1.3095             nan     0.0100    0.0035
     4        1.3027             nan     0.0100    0.0035
     5        1.2962             nan     0.0100    0.0034
     6        1.2891             nan     0.0100    0.0030
     7        1.2825             nan     0.0100    0.0035
     8        1.2759             nan     0.0100    0.0033
     9        1.2691             nan     0.0100    0.0031
    10        1.2627             nan     0.0100    0.0030
    20        1.2071             nan     0.0100    0.0023
    40        1.1183             nan     0.0100    0.0016
    60        1.0547             nan     0.0100    0.0013
    80        1.0067             nan     0.0100    0.0009
   100        0.9706             nan     0.0100    0.0008
   120        0.9434             nan     0.0100    0.0006
   140        0.9224             nan     0.0100    0.0001
   160        0.9042             nan     0.0100    0.0003
   180        0.8888             nan     0.0100    0.0002
   200        0.8765             nan     0.0100    0.0001
   220        0.8663             nan     0.0100    0.0001
   240        0.8556             nan     0.0100    0.0002
   260        0.8467             nan     0.0100    0.0002
   280        0.8383             nan     0.0100    0.0000
   300        0.8305             nan     0.0100    0.0001
   320        0.8239             nan     0.0100   -0.0000
   340        0.8173             nan     0.0100    0.0000
   360        0.8113             nan     0.0100   -0.0000
   380        0.8063             nan     0.0100    0.0000
   400        0.8009             nan     0.0100   -0.0001
   420        0.7964             nan     0.0100    0.0000
   440        0.7915             nan     0.0100   -0.0000
   460        0.7875             nan     0.0100   -0.0002
   480        0.7837             nan     0.0100   -0.0000
   500        0.7798             nan     0.0100    0.0001
   520        0.7762             nan     0.0100   -0.0000
   540        0.7725             nan     0.0100   -0.0001
   560        0.7692             nan     0.0100   -0.0000
   580        0.7658             nan     0.0100   -0.0000
   600        0.7624             nan     0.0100   -0.0000
   620        0.7595             nan     0.0100   -0.0000
   640        0.7566             nan     0.0100    0.0000
   660        0.7542             nan     0.0100    0.0000
   680        0.7514             nan     0.0100   -0.0000
   700        0.7486             nan     0.0100   -0.0000
   720        0.7459             nan     0.0100   -0.0001
   740        0.7431             nan     0.0100   -0.0000
   760        0.7403             nan     0.0100    0.0000
   780        0.7383             nan     0.0100   -0.0000
   800        0.7356             nan     0.0100    0.0000
   820        0.7334             nan     0.0100   -0.0002
   840        0.7311             nan     0.0100   -0.0001
   860        0.7291             nan     0.0100   -0.0001
   880        0.7270             nan     0.0100   -0.0001
   900        0.7250             nan     0.0100   -0.0001
   920        0.7229             nan     0.0100   -0.0000
   940        0.7210             nan     0.0100   -0.0001
   960        0.7193             nan     0.0100   -0.0001
   980        0.7174             nan     0.0100   -0.0000
  1000        0.7151             nan     0.0100   -0.0000
  1020        0.7131             nan     0.0100   -0.0001
  1040        0.7112             nan     0.0100   -0.0001
  1060        0.7090             nan     0.0100   -0.0001
  1080        0.7070             nan     0.0100   -0.0001
  1100        0.7054             nan     0.0100   -0.0001

- Fold05.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0040
     2        1.3161             nan     0.0100    0.0041
     3        1.3081             nan     0.0100    0.0040
     4        1.3008             nan     0.0100    0.0042
     5        1.2931             nan     0.0100    0.0038
     6        1.2857             nan     0.0100    0.0033
     7        1.2784             nan     0.0100    0.0033
     8        1.2714             nan     0.0100    0.0032
     9        1.2649             nan     0.0100    0.0030
    10        1.2579             nan     0.0100    0.0033
    20        1.1965             nan     0.0100    0.0027
    40        1.0987             nan     0.0100    0.0020
    60        1.0285             nan     0.0100    0.0014
    80        0.9767             nan     0.0100    0.0011
   100        0.9380             nan     0.0100    0.0006
   120        0.9090             nan     0.0100    0.0005
   140        0.8862             nan     0.0100    0.0004
   160        0.8671             nan     0.0100    0.0003
   180        0.8509             nan     0.0100    0.0002
   200        0.8363             nan     0.0100    0.0002
   220        0.8240             nan     0.0100    0.0002
   240        0.8136             nan     0.0100    0.0002
   260        0.8038             nan     0.0100    0.0001
   280        0.7944             nan     0.0100    0.0001
   300        0.7867             nan     0.0100    0.0000
   320        0.7788             nan     0.0100   -0.0000
   340        0.7723             nan     0.0100    0.0001
   360        0.7668             nan     0.0100   -0.0000
   380        0.7606             nan     0.0100   -0.0001
   400        0.7548             nan     0.0100   -0.0000
   420        0.7494             nan     0.0100   -0.0001
   440        0.7442             nan     0.0100    0.0001
   460        0.7394             nan     0.0100   -0.0001
   480        0.7354             nan     0.0100   -0.0001
   500        0.7313             nan     0.0100   -0.0001
   520        0.7276             nan     0.0100    0.0000
   540        0.7235             nan     0.0100   -0.0001
   560        0.7194             nan     0.0100   -0.0001
   580        0.7154             nan     0.0100   -0.0001
   600        0.7121             nan     0.0100   -0.0001
   620        0.7087             nan     0.0100   -0.0001
   640        0.7054             nan     0.0100   -0.0001
   660        0.7018             nan     0.0100   -0.0002
   680        0.6986             nan     0.0100   -0.0001
   700        0.6957             nan     0.0100   -0.0001
   720        0.6925             nan     0.0100   -0.0001
   740        0.6893             nan     0.0100   -0.0000
   760        0.6864             nan     0.0100   -0.0002
   780        0.6839             nan     0.0100   -0.0001
   800        0.6811             nan     0.0100   -0.0002
   820        0.6783             nan     0.0100   -0.0001
   840        0.6757             nan     0.0100   -0.0002
   860        0.6728             nan     0.0100   -0.0001
   880        0.6703             nan     0.0100   -0.0000
   900        0.6677             nan     0.0100   -0.0002
   920        0.6646             nan     0.0100   -0.0001
   940        0.6617             nan     0.0100   -0.0001
   960        0.6590             nan     0.0100   -0.0001
   980        0.6568             nan     0.0100   -0.0001
  1000        0.6543             nan     0.0100   -0.0001
  1020        0.6519             nan     0.0100   -0.0001
  1040        0.6494             nan     0.0100   -0.0002
  1060        0.6463             nan     0.0100   -0.0002
  1080        0.6440             nan     0.0100   -0.0001
  1100        0.6419             nan     0.0100   -0.0000

- Fold05.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2717             nan     0.1000    0.0276
     2        1.2269             nan     0.1000    0.0221
     3        1.1846             nan     0.1000    0.0198
     4        1.1529             nan     0.1000    0.0161
     5        1.1242             nan     0.1000    0.0133
     6        1.1056             nan     0.1000    0.0109
     7        1.0871             nan     0.1000    0.0087
     8        1.0708             nan     0.1000    0.0069
     9        1.0589             nan     0.1000    0.0050
    10        1.0437             nan     0.1000    0.0075
    20        0.9523             nan     0.1000    0.0026
    40        0.8772             nan     0.1000    0.0002
    60        0.8405             nan     0.1000    0.0003
    80        0.8157             nan     0.1000   -0.0002
   100        0.7981             nan     0.1000   -0.0008
   120        0.7879             nan     0.1000   -0.0005
   140        0.7762             nan     0.1000   -0.0007
   160        0.7697             nan     0.1000   -0.0004
   180        0.7637             nan     0.1000   -0.0004
   200        0.7571             nan     0.1000   -0.0005
   220        0.7513             nan     0.1000   -0.0010
   240        0.7472             nan     0.1000   -0.0013
   260        0.7419             nan     0.1000   -0.0005
   280        0.7374             nan     0.1000   -0.0006
   300        0.7345             nan     0.1000   -0.0005
   320        0.7320             nan     0.1000   -0.0013
   340        0.7266             nan     0.1000   -0.0007
   360        0.7239             nan     0.1000   -0.0004
   380        0.7215             nan     0.1000   -0.0016
   400        0.7162             nan     0.1000   -0.0004
   420        0.7136             nan     0.1000   -0.0004
   440        0.7109             nan     0.1000   -0.0007
   460        0.7087             nan     0.1000   -0.0004
   480        0.7060             nan     0.1000   -0.0010
   500        0.7039             nan     0.1000   -0.0008
   520        0.7005             nan     0.1000   -0.0007
   540        0.6980             nan     0.1000   -0.0008
   560        0.6949             nan     0.1000   -0.0007
   580        0.6917             nan     0.1000   -0.0005
   600        0.6894             nan     0.1000   -0.0003
   620        0.6883             nan     0.1000   -0.0012
   640        0.6857             nan     0.1000   -0.0008
   660        0.6836             nan     0.1000   -0.0006
   680        0.6812             nan     0.1000   -0.0008
   700        0.6795             nan     0.1000   -0.0005
   720        0.6773             nan     0.1000   -0.0008
   740        0.6757             nan     0.1000   -0.0006
   760        0.6739             nan     0.1000   -0.0009
   780        0.6715             nan     0.1000   -0.0006
   800        0.6695             nan     0.1000   -0.0004
   820        0.6680             nan     0.1000   -0.0004
   840        0.6654             nan     0.1000   -0.0007
   860        0.6648             nan     0.1000   -0.0006
   880        0.6637             nan     0.1000   -0.0004
   900        0.6617             nan     0.1000   -0.0011
   920        0.6609             nan     0.1000   -0.0006
   940        0.6589             nan     0.1000   -0.0006
   960        0.6568             nan     0.1000   -0.0024
   980        0.6561             nan     0.1000   -0.0005
  1000        0.6542             nan     0.1000   -0.0005
  1020        0.6530             nan     0.1000   -0.0011
  1040        0.6511             nan     0.1000   -0.0010
  1060        0.6495             nan     0.1000   -0.0010
  1080        0.6481             nan     0.1000   -0.0010
  1100        0.6462             nan     0.1000   -0.0004

- Fold05.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2572             nan     0.1000    0.0316
     2        1.2014             nan     0.1000    0.0279
     3        1.1536             nan     0.1000    0.0229
     4        1.1141             nan     0.1000    0.0201
     5        1.0768             nan     0.1000    0.0177
     6        1.0484             nan     0.1000    0.0123
     7        1.0239             nan     0.1000    0.0124
     8        1.0006             nan     0.1000    0.0102
     9        0.9837             nan     0.1000    0.0067
    10        0.9652             nan     0.1000    0.0080
    20        0.8737             nan     0.1000    0.0023
    40        0.8024             nan     0.1000    0.0003
    60        0.7663             nan     0.1000    0.0002
    80        0.7408             nan     0.1000   -0.0006
   100        0.7220             nan     0.1000   -0.0011
   120        0.7029             nan     0.1000   -0.0000
   140        0.6856             nan     0.1000   -0.0003
   160        0.6718             nan     0.1000   -0.0009
   180        0.6593             nan     0.1000   -0.0011
   200        0.6483             nan     0.1000   -0.0005
   220        0.6357             nan     0.1000   -0.0019
   240        0.6244             nan     0.1000   -0.0007
   260        0.6132             nan     0.1000   -0.0005
   280        0.6058             nan     0.1000   -0.0009
   300        0.5952             nan     0.1000   -0.0003
   320        0.5856             nan     0.1000   -0.0008
   340        0.5796             nan     0.1000   -0.0011
   360        0.5741             nan     0.1000   -0.0012
   380        0.5655             nan     0.1000   -0.0011
   400        0.5599             nan     0.1000   -0.0015
   420        0.5534             nan     0.1000   -0.0012
   440        0.5468             nan     0.1000   -0.0013
   460        0.5418             nan     0.1000   -0.0015
   480        0.5358             nan     0.1000   -0.0007
   500        0.5301             nan     0.1000   -0.0013
   520        0.5244             nan     0.1000   -0.0008
   540        0.5184             nan     0.1000   -0.0007
   560        0.5127             nan     0.1000   -0.0008
   580        0.5076             nan     0.1000   -0.0010
   600        0.5018             nan     0.1000   -0.0010
   620        0.4965             nan     0.1000   -0.0008
   640        0.4910             nan     0.1000   -0.0010
   660        0.4871             nan     0.1000   -0.0008
   680        0.4812             nan     0.1000   -0.0005
   700        0.4783             nan     0.1000   -0.0010
   720        0.4726             nan     0.1000   -0.0011
   740        0.4688             nan     0.1000   -0.0009
   760        0.4658             nan     0.1000   -0.0006
   780        0.4621             nan     0.1000   -0.0006
   800        0.4574             nan     0.1000   -0.0004
   820        0.4530             nan     0.1000   -0.0011
   840        0.4486             nan     0.1000   -0.0010
   860        0.4450             nan     0.1000   -0.0013
   880        0.4413             nan     0.1000   -0.0007
   900        0.4383             nan     0.1000   -0.0006
   920        0.4336             nan     0.1000   -0.0006
   940        0.4328             nan     0.1000   -0.0007
   960        0.4291             nan     0.1000   -0.0007
   980        0.4242             nan     0.1000   -0.0012
  1000        0.4207             nan     0.1000   -0.0013
  1020        0.4180             nan     0.1000   -0.0007
  1040        0.4139             nan     0.1000   -0.0005
  1060        0.4108             nan     0.1000   -0.0012
  1080        0.4089             nan     0.1000   -0.0005
  1100        0.4055             nan     0.1000   -0.0013

- Fold05.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2542             nan     0.1000    0.0367
     2        1.1910             nan     0.1000    0.0302
     3        1.1378             nan     0.1000    0.0245
     4        1.0933             nan     0.1000    0.0219
     5        1.0567             nan     0.1000    0.0191
     6        1.0246             nan     0.1000    0.0152
     7        0.9983             nan     0.1000    0.0114
     8        0.9746             nan     0.1000    0.0104
     9        0.9562             nan     0.1000    0.0088
    10        0.9381             nan     0.1000    0.0088
    20        0.8421             nan     0.1000    0.0009
    40        0.7673             nan     0.1000   -0.0005
    60        0.7151             nan     0.1000    0.0002
    80        0.6821             nan     0.1000   -0.0012
   100        0.6556             nan     0.1000   -0.0006
   120        0.6350             nan     0.1000   -0.0009
   140        0.6133             nan     0.1000   -0.0010
   160        0.5964             nan     0.1000   -0.0016
   180        0.5789             nan     0.1000   -0.0014
   200        0.5674             nan     0.1000   -0.0006
   220        0.5539             nan     0.1000   -0.0020
   240        0.5419             nan     0.1000   -0.0014
   260        0.5284             nan     0.1000   -0.0011
   280        0.5199             nan     0.1000   -0.0012
   300        0.5077             nan     0.1000   -0.0014
   320        0.4984             nan     0.1000   -0.0020
   340        0.4897             nan     0.1000   -0.0011
   360        0.4785             nan     0.1000   -0.0009
   380        0.4725             nan     0.1000   -0.0006
   400        0.4629             nan     0.1000   -0.0018
   420        0.4549             nan     0.1000   -0.0008
   440        0.4441             nan     0.1000   -0.0016
   460        0.4371             nan     0.1000   -0.0012
   480        0.4268             nan     0.1000   -0.0009
   500        0.4174             nan     0.1000   -0.0009
   520        0.4078             nan     0.1000   -0.0005
   540        0.4002             nan     0.1000   -0.0010
   560        0.3910             nan     0.1000   -0.0005
   580        0.3840             nan     0.1000   -0.0010
   600        0.3767             nan     0.1000   -0.0017
   620        0.3691             nan     0.1000   -0.0010
   640        0.3629             nan     0.1000   -0.0009
   660        0.3576             nan     0.1000   -0.0006
   680        0.3519             nan     0.1000   -0.0012
   700        0.3452             nan     0.1000   -0.0013
   720        0.3391             nan     0.1000   -0.0008
   740        0.3338             nan     0.1000   -0.0016
   760        0.3304             nan     0.1000   -0.0010
   780        0.3241             nan     0.1000   -0.0008
   800        0.3201             nan     0.1000   -0.0006
   820        0.3148             nan     0.1000   -0.0014
   840        0.3104             nan     0.1000   -0.0008
   860        0.3063             nan     0.1000   -0.0006
   880        0.3036             nan     0.1000   -0.0012
   900        0.3002             nan     0.1000   -0.0005
   920        0.2951             nan     0.1000   -0.0015
   940        0.2906             nan     0.1000   -0.0010
   960        0.2861             nan     0.1000   -0.0004
   980        0.2821             nan     0.1000   -0.0007
  1000        0.2783             nan     0.1000   -0.0011
  1020        0.2750             nan     0.1000   -0.0012
  1040        0.2712             nan     0.1000   -0.0010
  1060        0.2691             nan     0.1000   -0.0007
  1080        0.2651             nan     0.1000   -0.0012
  1100        0.2622             nan     0.1000   -0.0007

- Fold05.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0029
     2        1.3203             nan     0.0100    0.0028
     3        1.3149             nan     0.0100    0.0028
     4        1.3093             nan     0.0100    0.0027
     5        1.3041             nan     0.0100    0.0027
     6        1.2992             nan     0.0100    0.0026
     7        1.2945             nan     0.0100    0.0025
     8        1.2893             nan     0.0100    0.0025
     9        1.2843             nan     0.0100    0.0024
    10        1.2798             nan     0.0100    0.0024
    20        1.2367             nan     0.0100    0.0020
    40        1.1690             nan     0.0100    0.0014
    60        1.1232             nan     0.0100    0.0010
    80        1.0877             nan     0.0100    0.0008
   100        1.0586             nan     0.0100    0.0006
   120        1.0352             nan     0.0100    0.0005
   140        1.0164             nan     0.0100    0.0004
   160        0.9990             nan     0.0100    0.0002
   180        0.9844             nan     0.0100    0.0003
   200        0.9707             nan     0.0100    0.0002
   220        0.9594             nan     0.0100    0.0002
   240        0.9488             nan     0.0100    0.0001
   260        0.9391             nan     0.0100    0.0001
   280        0.9307             nan     0.0100    0.0001
   300        0.9232             nan     0.0100    0.0001
   320        0.9165             nan     0.0100    0.0001
   340        0.9097             nan     0.0100    0.0001
   360        0.9039             nan     0.0100    0.0001
   380        0.8980             nan     0.0100    0.0000
   400        0.8928             nan     0.0100    0.0001
   420        0.8879             nan     0.0100   -0.0000
   440        0.8833             nan     0.0100    0.0000
   460        0.8786             nan     0.0100    0.0000
   480        0.8742             nan     0.0100   -0.0000
   500        0.8699             nan     0.0100   -0.0001
   520        0.8664             nan     0.0100    0.0000
   540        0.8627             nan     0.0100   -0.0001
   560        0.8592             nan     0.0100   -0.0000
   580        0.8560             nan     0.0100   -0.0000
   600        0.8528             nan     0.0100    0.0000
   620        0.8498             nan     0.0100   -0.0002
   640        0.8468             nan     0.0100    0.0000
   660        0.8443             nan     0.0100   -0.0001
   680        0.8414             nan     0.0100    0.0000
   700        0.8389             nan     0.0100   -0.0000
   720        0.8365             nan     0.0100    0.0000
   740        0.8340             nan     0.0100    0.0000
   760        0.8317             nan     0.0100   -0.0000
   780        0.8295             nan     0.0100   -0.0000
   800        0.8274             nan     0.0100   -0.0001
   820        0.8252             nan     0.0100   -0.0000
   840        0.8232             nan     0.0100    0.0000
   860        0.8212             nan     0.0100   -0.0001
   880        0.8191             nan     0.0100   -0.0000
   900        0.8174             nan     0.0100   -0.0000
   920        0.8157             nan     0.0100   -0.0000
   940        0.8142             nan     0.0100    0.0000
   960        0.8125             nan     0.0100    0.0000
   980        0.8108             nan     0.0100   -0.0001
  1000        0.8093             nan     0.0100   -0.0000
  1020        0.8078             nan     0.0100   -0.0000
  1040        0.8064             nan     0.0100   -0.0000
  1060        0.8050             nan     0.0100   -0.0000
  1080        0.8039             nan     0.0100   -0.0000
  1100        0.8027             nan     0.0100   -0.0001

- Fold06.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0036
     2        1.3176             nan     0.0100    0.0036
     3        1.3102             nan     0.0100    0.0034
     4        1.3033             nan     0.0100    0.0032
     5        1.2966             nan     0.0100    0.0034
     6        1.2897             nan     0.0100    0.0031
     7        1.2832             nan     0.0100    0.0029
     8        1.2768             nan     0.0100    0.0030
     9        1.2708             nan     0.0100    0.0031
    10        1.2648             nan     0.0100    0.0030
    20        1.2096             nan     0.0100    0.0024
    40        1.1230             nan     0.0100    0.0018
    60        1.0612             nan     0.0100    0.0011
    80        1.0164             nan     0.0100    0.0009
   100        0.9807             nan     0.0100    0.0005
   120        0.9537             nan     0.0100    0.0003
   140        0.9314             nan     0.0100    0.0003
   160        0.9122             nan     0.0100    0.0003
   180        0.8971             nan     0.0100    0.0004
   200        0.8845             nan     0.0100    0.0001
   220        0.8730             nan     0.0100    0.0001
   240        0.8622             nan     0.0100    0.0000
   260        0.8533             nan     0.0100    0.0000
   280        0.8446             nan     0.0100   -0.0000
   300        0.8371             nan     0.0100    0.0001
   320        0.8296             nan     0.0100    0.0000
   340        0.8231             nan     0.0100    0.0000
   360        0.8175             nan     0.0100   -0.0000
   380        0.8118             nan     0.0100   -0.0000
   400        0.8066             nan     0.0100   -0.0000
   420        0.8018             nan     0.0100   -0.0000
   440        0.7975             nan     0.0100   -0.0000
   460        0.7929             nan     0.0100   -0.0001
   480        0.7884             nan     0.0100   -0.0000
   500        0.7846             nan     0.0100   -0.0001
   520        0.7810             nan     0.0100   -0.0000
   540        0.7778             nan     0.0100   -0.0001
   560        0.7750             nan     0.0100   -0.0000
   580        0.7717             nan     0.0100   -0.0000
   600        0.7683             nan     0.0100   -0.0000
   620        0.7648             nan     0.0100   -0.0001
   640        0.7620             nan     0.0100   -0.0001
   660        0.7592             nan     0.0100   -0.0001
   680        0.7566             nan     0.0100   -0.0002
   700        0.7539             nan     0.0100   -0.0001
   720        0.7517             nan     0.0100   -0.0001
   740        0.7489             nan     0.0100   -0.0000
   760        0.7461             nan     0.0100   -0.0000
   780        0.7439             nan     0.0100   -0.0001
   800        0.7419             nan     0.0100   -0.0002
   820        0.7393             nan     0.0100   -0.0000
   840        0.7367             nan     0.0100   -0.0001
   860        0.7347             nan     0.0100   -0.0001
   880        0.7325             nan     0.0100   -0.0000
   900        0.7301             nan     0.0100   -0.0001
   920        0.7279             nan     0.0100   -0.0001
   940        0.7260             nan     0.0100   -0.0001
   960        0.7240             nan     0.0100   -0.0001
   980        0.7220             nan     0.0100   -0.0001
  1000        0.7196             nan     0.0100   -0.0001
  1020        0.7175             nan     0.0100   -0.0000
  1040        0.7158             nan     0.0100   -0.0000
  1060        0.7135             nan     0.0100   -0.0000
  1080        0.7118             nan     0.0100   -0.0001
  1100        0.7104             nan     0.0100   -0.0001

- Fold06.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3240             nan     0.0100    0.0036
     2        1.3164             nan     0.0100    0.0037
     3        1.3089             nan     0.0100    0.0037
     4        1.3020             nan     0.0100    0.0036
     5        1.2947             nan     0.0100    0.0034
     6        1.2880             nan     0.0100    0.0032
     7        1.2811             nan     0.0100    0.0036
     8        1.2737             nan     0.0100    0.0035
     9        1.2673             nan     0.0100    0.0030
    10        1.2610             nan     0.0100    0.0031
    20        1.1990             nan     0.0100    0.0026
    40        1.1049             nan     0.0100    0.0018
    60        1.0377             nan     0.0100    0.0014
    80        0.9870             nan     0.0100    0.0011
   100        0.9491             nan     0.0100    0.0008
   120        0.9196             nan     0.0100    0.0004
   140        0.8954             nan     0.0100    0.0003
   160        0.8753             nan     0.0100    0.0004
   180        0.8593             nan     0.0100   -0.0000
   200        0.8462             nan     0.0100    0.0002
   220        0.8337             nan     0.0100   -0.0000
   240        0.8243             nan     0.0100    0.0001
   260        0.8141             nan     0.0100    0.0002
   280        0.8054             nan     0.0100    0.0000
   300        0.7972             nan     0.0100    0.0000
   320        0.7899             nan     0.0100    0.0001
   340        0.7832             nan     0.0100    0.0000
   360        0.7771             nan     0.0100   -0.0001
   380        0.7702             nan     0.0100    0.0001
   400        0.7648             nan     0.0100    0.0001
   420        0.7591             nan     0.0100   -0.0001
   440        0.7538             nan     0.0100   -0.0002
   460        0.7487             nan     0.0100   -0.0001
   480        0.7445             nan     0.0100   -0.0001
   500        0.7396             nan     0.0100   -0.0001
   520        0.7355             nan     0.0100   -0.0001
   540        0.7312             nan     0.0100   -0.0001
   560        0.7270             nan     0.0100   -0.0001
   580        0.7234             nan     0.0100   -0.0001
   600        0.7196             nan     0.0100   -0.0001
   620        0.7157             nan     0.0100   -0.0002
   640        0.7121             nan     0.0100   -0.0001
   660        0.7091             nan     0.0100   -0.0001
   680        0.7058             nan     0.0100   -0.0001
   700        0.7027             nan     0.0100   -0.0003
   720        0.6999             nan     0.0100   -0.0002
   740        0.6967             nan     0.0100   -0.0001
   760        0.6936             nan     0.0100   -0.0002
   780        0.6905             nan     0.0100   -0.0001
   800        0.6872             nan     0.0100   -0.0001
   820        0.6848             nan     0.0100   -0.0001
   840        0.6830             nan     0.0100   -0.0003
   860        0.6799             nan     0.0100   -0.0000
   880        0.6774             nan     0.0100   -0.0001
   900        0.6746             nan     0.0100   -0.0001
   920        0.6724             nan     0.0100   -0.0001
   940        0.6697             nan     0.0100   -0.0001
   960        0.6669             nan     0.0100   -0.0000
   980        0.6645             nan     0.0100   -0.0001
  1000        0.6617             nan     0.0100   -0.0000
  1020        0.6593             nan     0.0100   -0.0001
  1040        0.6569             nan     0.0100   -0.0001
  1060        0.6542             nan     0.0100   -0.0001
  1080        0.6521             nan     0.0100   -0.0001
  1100        0.6501             nan     0.0100   -0.0001

- Fold06.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2750             nan     0.1000    0.0264
     2        1.2333             nan     0.1000    0.0228
     3        1.1986             nan     0.1000    0.0188
     4        1.1640             nan     0.1000    0.0152
     5        1.1412             nan     0.1000    0.0122
     6        1.1201             nan     0.1000    0.0104
     7        1.0996             nan     0.1000    0.0088
     8        1.0824             nan     0.1000    0.0069
     9        1.0675             nan     0.1000    0.0071
    10        1.0556             nan     0.1000    0.0056
    20        0.9677             nan     0.1000    0.0019
    40        0.8932             nan     0.1000    0.0005
    60        0.8521             nan     0.1000   -0.0001
    80        0.8263             nan     0.1000   -0.0013
   100        0.8077             nan     0.1000   -0.0005
   120        0.7944             nan     0.1000   -0.0001
   140        0.7841             nan     0.1000   -0.0004
   160        0.7758             nan     0.1000   -0.0006
   180        0.7687             nan     0.1000   -0.0005
   200        0.7623             nan     0.1000   -0.0002
   220        0.7559             nan     0.1000   -0.0009
   240        0.7524             nan     0.1000   -0.0010
   260        0.7482             nan     0.1000   -0.0009
   280        0.7431             nan     0.1000   -0.0010
   300        0.7401             nan     0.1000   -0.0005
   320        0.7365             nan     0.1000   -0.0006
   340        0.7322             nan     0.1000   -0.0016
   360        0.7293             nan     0.1000   -0.0011
   380        0.7244             nan     0.1000   -0.0008
   400        0.7223             nan     0.1000   -0.0009
   420        0.7188             nan     0.1000   -0.0013
   440        0.7151             nan     0.1000   -0.0002
   460        0.7131             nan     0.1000   -0.0008
   480        0.7120             nan     0.1000   -0.0011
   500        0.7078             nan     0.1000   -0.0004
   520        0.7046             nan     0.1000   -0.0011
   540        0.7024             nan     0.1000   -0.0007
   560        0.6985             nan     0.1000   -0.0006
   580        0.6966             nan     0.1000   -0.0010
   600        0.6940             nan     0.1000   -0.0008
   620        0.6915             nan     0.1000   -0.0007
   640        0.6897             nan     0.1000   -0.0005
   660        0.6876             nan     0.1000   -0.0005
   680        0.6859             nan     0.1000   -0.0002
   700        0.6833             nan     0.1000    0.0000
   720        0.6814             nan     0.1000   -0.0010
   740        0.6797             nan     0.1000   -0.0013
   760        0.6781             nan     0.1000   -0.0010
   780        0.6754             nan     0.1000   -0.0001
   800        0.6749             nan     0.1000   -0.0013
   820        0.6734             nan     0.1000   -0.0008
   840        0.6703             nan     0.1000   -0.0003
   860        0.6690             nan     0.1000   -0.0003
   880        0.6678             nan     0.1000   -0.0008
   900        0.6652             nan     0.1000   -0.0010
   920        0.6631             nan     0.1000   -0.0007
   940        0.6613             nan     0.1000   -0.0015
   960        0.6601             nan     0.1000   -0.0007
   980        0.6599             nan     0.1000   -0.0009
  1000        0.6586             nan     0.1000   -0.0012
  1020        0.6559             nan     0.1000   -0.0005
  1040        0.6551             nan     0.1000   -0.0010
  1060        0.6543             nan     0.1000   -0.0009
  1080        0.6530             nan     0.1000   -0.0006
  1100        0.6513             nan     0.1000   -0.0002

- Fold06.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2594             nan     0.1000    0.0336
     2        1.2043             nan     0.1000    0.0270
     3        1.1531             nan     0.1000    0.0244
     4        1.1177             nan     0.1000    0.0180
     5        1.0844             nan     0.1000    0.0166
     6        1.0587             nan     0.1000    0.0141
     7        1.0384             nan     0.1000    0.0108
     8        1.0153             nan     0.1000    0.0101
     9        1.0006             nan     0.1000    0.0062
    10        0.9823             nan     0.1000    0.0063
    20        0.8847             nan     0.1000    0.0019
    40        0.8109             nan     0.1000   -0.0003
    60        0.7695             nan     0.1000   -0.0003
    80        0.7415             nan     0.1000   -0.0012
   100        0.7202             nan     0.1000   -0.0023
   120        0.7040             nan     0.1000   -0.0009
   140        0.6839             nan     0.1000   -0.0001
   160        0.6687             nan     0.1000   -0.0003
   180        0.6540             nan     0.1000   -0.0010
   200        0.6395             nan     0.1000   -0.0008
   220        0.6303             nan     0.1000   -0.0003
   240        0.6223             nan     0.1000   -0.0002
   260        0.6128             nan     0.1000   -0.0009
   280        0.6039             nan     0.1000   -0.0009
   300        0.5959             nan     0.1000   -0.0014
   320        0.5869             nan     0.1000   -0.0010
   340        0.5774             nan     0.1000   -0.0012
   360        0.5690             nan     0.1000   -0.0008
   380        0.5598             nan     0.1000   -0.0006
   400        0.5530             nan     0.1000   -0.0011
   420        0.5467             nan     0.1000   -0.0009
   440        0.5404             nan     0.1000   -0.0008
   460        0.5367             nan     0.1000   -0.0008
   480        0.5300             nan     0.1000   -0.0015
   500        0.5235             nan     0.1000   -0.0003
   520        0.5193             nan     0.1000   -0.0026
   540        0.5134             nan     0.1000   -0.0010
   560        0.5070             nan     0.1000   -0.0011
   580        0.5019             nan     0.1000   -0.0014
   600        0.4963             nan     0.1000   -0.0003
   620        0.4924             nan     0.1000   -0.0006
   640        0.4886             nan     0.1000   -0.0014
   660        0.4830             nan     0.1000   -0.0008
   680        0.4783             nan     0.1000   -0.0010
   700        0.4722             nan     0.1000   -0.0010
   720        0.4671             nan     0.1000   -0.0015
   740        0.4624             nan     0.1000   -0.0015
   760        0.4577             nan     0.1000   -0.0009
   780        0.4540             nan     0.1000   -0.0009
   800        0.4506             nan     0.1000   -0.0008
   820        0.4455             nan     0.1000   -0.0007
   840        0.4412             nan     0.1000   -0.0007
   860        0.4391             nan     0.1000   -0.0007
   880        0.4347             nan     0.1000   -0.0013
   900        0.4312             nan     0.1000   -0.0008
   920        0.4268             nan     0.1000   -0.0011
   940        0.4236             nan     0.1000   -0.0004
   960        0.4203             nan     0.1000   -0.0008
   980        0.4176             nan     0.1000   -0.0008
  1000        0.4149             nan     0.1000   -0.0007
  1020        0.4109             nan     0.1000   -0.0012
  1040        0.4078             nan     0.1000   -0.0008
  1060        0.4046             nan     0.1000   -0.0006
  1080        0.4015             nan     0.1000   -0.0006
  1100        0.3995             nan     0.1000   -0.0005

- Fold06.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2556             nan     0.1000    0.0366
     2        1.1916             nan     0.1000    0.0313
     3        1.1399             nan     0.1000    0.0231
     4        1.0955             nan     0.1000    0.0222
     5        1.0561             nan     0.1000    0.0171
     6        1.0289             nan     0.1000    0.0130
     7        1.0044             nan     0.1000    0.0111
     8        0.9803             nan     0.1000    0.0099
     9        0.9616             nan     0.1000    0.0061
    10        0.9434             nan     0.1000    0.0078
    20        0.8514             nan     0.1000    0.0004
    40        0.7746             nan     0.1000    0.0001
    60        0.7278             nan     0.1000   -0.0003
    80        0.6946             nan     0.1000   -0.0017
   100        0.6690             nan     0.1000   -0.0011
   120        0.6412             nan     0.1000   -0.0017
   140        0.6225             nan     0.1000   -0.0014
   160        0.6075             nan     0.1000   -0.0016
   180        0.5853             nan     0.1000   -0.0004
   200        0.5643             nan     0.1000   -0.0000
   220        0.5493             nan     0.1000   -0.0007
   240        0.5341             nan     0.1000   -0.0010
   260        0.5228             nan     0.1000   -0.0006
   280        0.5110             nan     0.1000   -0.0009
   300        0.5011             nan     0.1000   -0.0015
   320        0.4918             nan     0.1000   -0.0016
   340        0.4806             nan     0.1000   -0.0015
   360        0.4719             nan     0.1000   -0.0010
   380        0.4607             nan     0.1000   -0.0015
   400        0.4500             nan     0.1000   -0.0012
   420        0.4398             nan     0.1000   -0.0004
   440        0.4330             nan     0.1000   -0.0006
   460        0.4264             nan     0.1000   -0.0009
   480        0.4165             nan     0.1000   -0.0013
   500        0.4103             nan     0.1000   -0.0013
   520        0.4021             nan     0.1000   -0.0008
   540        0.3958             nan     0.1000   -0.0013
   560        0.3891             nan     0.1000   -0.0019
   580        0.3854             nan     0.1000   -0.0018
   600        0.3795             nan     0.1000   -0.0018
   620        0.3751             nan     0.1000   -0.0006
   640        0.3702             nan     0.1000   -0.0005
   660        0.3643             nan     0.1000   -0.0009
   680        0.3587             nan     0.1000   -0.0007
   700        0.3527             nan     0.1000   -0.0005
   720        0.3488             nan     0.1000   -0.0011
   740        0.3444             nan     0.1000   -0.0016
   760        0.3419             nan     0.1000   -0.0009
   780        0.3357             nan     0.1000   -0.0006
   800        0.3304             nan     0.1000   -0.0005
   820        0.3268             nan     0.1000   -0.0009
   840        0.3220             nan     0.1000   -0.0010
   860        0.3191             nan     0.1000   -0.0007
   880        0.3159             nan     0.1000   -0.0008
   900        0.3140             nan     0.1000   -0.0011
   920        0.3105             nan     0.1000   -0.0012
   940        0.3062             nan     0.1000   -0.0009
   960        0.3010             nan     0.1000   -0.0011
   980        0.2975             nan     0.1000   -0.0005
  1000        0.2938             nan     0.1000   -0.0010
  1020        0.2919             nan     0.1000   -0.0007
  1040        0.2874             nan     0.1000   -0.0008
  1060        0.2856             nan     0.1000   -0.0009
  1080        0.2826             nan     0.1000   -0.0008
  1100        0.2801             nan     0.1000   -0.0009

- Fold06.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3257             nan     0.0100    0.0030
     2        1.3195             nan     0.0100    0.0029
     3        1.3134             nan     0.0100    0.0027
     4        1.3086             nan     0.0100    0.0027
     5        1.3028             nan     0.0100    0.0028
     6        1.2969             nan     0.0100    0.0027
     7        1.2911             nan     0.0100    0.0027
     8        1.2857             nan     0.0100    0.0026
     9        1.2808             nan     0.0100    0.0026
    10        1.2755             nan     0.0100    0.0025
    20        1.2293             nan     0.0100    0.0019
    40        1.1586             nan     0.0100    0.0014
    60        1.1106             nan     0.0100    0.0011
    80        1.0739             nan     0.0100    0.0008
   100        1.0438             nan     0.0100    0.0006
   120        1.0196             nan     0.0100    0.0005
   140        0.9992             nan     0.0100    0.0004
   160        0.9821             nan     0.0100    0.0003
   180        0.9670             nan     0.0100    0.0002
   200        0.9538             nan     0.0100    0.0002
   220        0.9421             nan     0.0100    0.0001
   240        0.9316             nan     0.0100    0.0002
   260        0.9221             nan     0.0100    0.0002
   280        0.9133             nan     0.0100    0.0001
   300        0.9052             nan     0.0100    0.0002
   320        0.8985             nan     0.0100    0.0000
   340        0.8921             nan     0.0100    0.0001
   360        0.8862             nan     0.0100    0.0001
   380        0.8805             nan     0.0100    0.0000
   400        0.8756             nan     0.0100   -0.0000
   420        0.8708             nan     0.0100   -0.0001
   440        0.8665             nan     0.0100    0.0000
   460        0.8621             nan     0.0100    0.0000
   480        0.8586             nan     0.0100    0.0000
   500        0.8547             nan     0.0100    0.0000
   520        0.8510             nan     0.0100    0.0000
   540        0.8478             nan     0.0100    0.0000
   560        0.8443             nan     0.0100    0.0000
   580        0.8414             nan     0.0100   -0.0001
   600        0.8385             nan     0.0100   -0.0000
   620        0.8359             nan     0.0100   -0.0001
   640        0.8331             nan     0.0100   -0.0000
   660        0.8303             nan     0.0100   -0.0001
   680        0.8278             nan     0.0100    0.0000
   700        0.8253             nan     0.0100    0.0000
   720        0.8227             nan     0.0100   -0.0000
   740        0.8201             nan     0.0100   -0.0000
   760        0.8180             nan     0.0100    0.0000
   780        0.8157             nan     0.0100   -0.0001
   800        0.8134             nan     0.0100   -0.0000
   820        0.8115             nan     0.0100   -0.0001
   840        0.8094             nan     0.0100   -0.0000
   860        0.8074             nan     0.0100   -0.0000
   880        0.8058             nan     0.0100   -0.0001
   900        0.8039             nan     0.0100    0.0000
   920        0.8021             nan     0.0100   -0.0000
   940        0.8004             nan     0.0100   -0.0002
   960        0.7988             nan     0.0100   -0.0001
   980        0.7971             nan     0.0100   -0.0000
  1000        0.7957             nan     0.0100   -0.0000
  1020        0.7942             nan     0.0100   -0.0001
  1040        0.7928             nan     0.0100   -0.0000
  1060        0.7916             nan     0.0100   -0.0001
  1080        0.7904             nan     0.0100   -0.0000
  1100        0.7889             nan     0.0100   -0.0001

- Fold07.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3248             nan     0.0100    0.0038
     2        1.3173             nan     0.0100    0.0036
     3        1.3103             nan     0.0100    0.0036
     4        1.3029             nan     0.0100    0.0035
     5        1.2958             nan     0.0100    0.0032
     6        1.2887             nan     0.0100    0.0033
     7        1.2823             nan     0.0100    0.0030
     8        1.2756             nan     0.0100    0.0031
     9        1.2697             nan     0.0100    0.0031
    10        1.2635             nan     0.0100    0.0033
    20        1.2071             nan     0.0100    0.0024
    40        1.1191             nan     0.0100    0.0019
    60        1.0552             nan     0.0100    0.0012
    80        1.0074             nan     0.0100    0.0009
   100        0.9708             nan     0.0100    0.0007
   120        0.9427             nan     0.0100    0.0006
   140        0.9206             nan     0.0100    0.0003
   160        0.9027             nan     0.0100    0.0002
   180        0.8864             nan     0.0100    0.0002
   200        0.8737             nan     0.0100    0.0003
   220        0.8619             nan     0.0100    0.0001
   240        0.8519             nan     0.0100    0.0001
   260        0.8434             nan     0.0100    0.0001
   280        0.8345             nan     0.0100    0.0001
   300        0.8272             nan     0.0100    0.0001
   320        0.8201             nan     0.0100    0.0002
   340        0.8145             nan     0.0100    0.0000
   360        0.8089             nan     0.0100    0.0000
   380        0.8029             nan     0.0100    0.0001
   400        0.7982             nan     0.0100   -0.0000
   420        0.7936             nan     0.0100    0.0001
   440        0.7893             nan     0.0100   -0.0001
   460        0.7853             nan     0.0100   -0.0000
   480        0.7816             nan     0.0100   -0.0000
   500        0.7774             nan     0.0100    0.0001
   520        0.7736             nan     0.0100   -0.0000
   540        0.7695             nan     0.0100   -0.0000
   560        0.7656             nan     0.0100   -0.0001
   580        0.7624             nan     0.0100   -0.0001
   600        0.7590             nan     0.0100    0.0000
   620        0.7562             nan     0.0100   -0.0000
   640        0.7530             nan     0.0100   -0.0001
   660        0.7501             nan     0.0100   -0.0001
   680        0.7473             nan     0.0100   -0.0001
   700        0.7445             nan     0.0100   -0.0000
   720        0.7419             nan     0.0100    0.0000
   740        0.7396             nan     0.0100   -0.0001
   760        0.7373             nan     0.0100   -0.0001
   780        0.7351             nan     0.0100   -0.0001
   800        0.7325             nan     0.0100   -0.0001
   820        0.7304             nan     0.0100   -0.0001
   840        0.7278             nan     0.0100   -0.0001
   860        0.7256             nan     0.0100   -0.0001
   880        0.7238             nan     0.0100   -0.0001
   900        0.7216             nan     0.0100   -0.0000
   920        0.7193             nan     0.0100   -0.0001
   940        0.7171             nan     0.0100   -0.0001
   960        0.7153             nan     0.0100   -0.0001
   980        0.7131             nan     0.0100   -0.0001
  1000        0.7108             nan     0.0100   -0.0000
  1020        0.7092             nan     0.0100   -0.0001
  1040        0.7074             nan     0.0100   -0.0001
  1060        0.7055             nan     0.0100   -0.0002
  1080        0.7035             nan     0.0100   -0.0001
  1100        0.7019             nan     0.0100   -0.0001

- Fold07.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3245             nan     0.0100    0.0039
     2        1.3160             nan     0.0100    0.0042
     3        1.3085             nan     0.0100    0.0036
     4        1.3007             nan     0.0100    0.0035
     5        1.2928             nan     0.0100    0.0037
     6        1.2858             nan     0.0100    0.0032
     7        1.2787             nan     0.0100    0.0035
     8        1.2722             nan     0.0100    0.0034
     9        1.2654             nan     0.0100    0.0032
    10        1.2588             nan     0.0100    0.0031
    20        1.1953             nan     0.0100    0.0025
    40        1.0986             nan     0.0100    0.0018
    60        1.0280             nan     0.0100    0.0014
    80        0.9758             nan     0.0100    0.0010
   100        0.9378             nan     0.0100    0.0005
   120        0.9082             nan     0.0100    0.0004
   140        0.8852             nan     0.0100    0.0004
   160        0.8673             nan     0.0100    0.0002
   180        0.8514             nan     0.0100    0.0003
   200        0.8370             nan     0.0100    0.0003
   220        0.8244             nan     0.0100    0.0002
   240        0.8130             nan     0.0100   -0.0000
   260        0.8030             nan     0.0100    0.0001
   280        0.7946             nan     0.0100    0.0000
   300        0.7870             nan     0.0100   -0.0001
   320        0.7800             nan     0.0100    0.0001
   340        0.7730             nan     0.0100   -0.0000
   360        0.7671             nan     0.0100   -0.0000
   380        0.7615             nan     0.0100    0.0000
   400        0.7561             nan     0.0100   -0.0000
   420        0.7511             nan     0.0100   -0.0001
   440        0.7464             nan     0.0100   -0.0001
   460        0.7421             nan     0.0100   -0.0001
   480        0.7370             nan     0.0100   -0.0001
   500        0.7329             nan     0.0100   -0.0001
   520        0.7284             nan     0.0100   -0.0001
   540        0.7245             nan     0.0100   -0.0001
   560        0.7205             nan     0.0100   -0.0001
   580        0.7163             nan     0.0100   -0.0000
   600        0.7123             nan     0.0100   -0.0001
   620        0.7088             nan     0.0100   -0.0000
   640        0.7049             nan     0.0100   -0.0001
   660        0.7013             nan     0.0100   -0.0002
   680        0.6976             nan     0.0100   -0.0000
   700        0.6944             nan     0.0100   -0.0001
   720        0.6915             nan     0.0100   -0.0001
   740        0.6883             nan     0.0100   -0.0002
   760        0.6849             nan     0.0100   -0.0000
   780        0.6818             nan     0.0100   -0.0002
   800        0.6785             nan     0.0100   -0.0000
   820        0.6757             nan     0.0100   -0.0001
   840        0.6731             nan     0.0100   -0.0001
   860        0.6701             nan     0.0100   -0.0001
   880        0.6678             nan     0.0100   -0.0001
   900        0.6648             nan     0.0100   -0.0001
   920        0.6620             nan     0.0100   -0.0000
   940        0.6589             nan     0.0100   -0.0002
   960        0.6564             nan     0.0100   -0.0001
   980        0.6538             nan     0.0100   -0.0001
  1000        0.6509             nan     0.0100   -0.0001
  1020        0.6487             nan     0.0100   -0.0001
  1040        0.6459             nan     0.0100   -0.0000
  1060        0.6434             nan     0.0100   -0.0001
  1080        0.6412             nan     0.0100   -0.0001
  1100        0.6386             nan     0.0100   -0.0001

- Fold07.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2750             nan     0.1000    0.0286
     2        1.2267             nan     0.1000    0.0236
     3        1.1912             nan     0.1000    0.0189
     4        1.1570             nan     0.1000    0.0158
     5        1.1324             nan     0.1000    0.0133
     6        1.1077             nan     0.1000    0.0110
     7        1.0910             nan     0.1000    0.0090
     8        1.0756             nan     0.1000    0.0076
     9        1.0589             nan     0.1000    0.0069
    10        1.0468             nan     0.1000    0.0062
    20        0.9499             nan     0.1000    0.0020
    40        0.8764             nan     0.1000   -0.0008
    60        0.8380             nan     0.1000   -0.0006
    80        0.8127             nan     0.1000   -0.0008
   100        0.7946             nan     0.1000   -0.0008
   120        0.7815             nan     0.1000   -0.0007
   140        0.7700             nan     0.1000   -0.0015
   160        0.7628             nan     0.1000   -0.0005
   180        0.7568             nan     0.1000   -0.0008
   200        0.7490             nan     0.1000   -0.0006
   220        0.7438             nan     0.1000   -0.0003
   240        0.7392             nan     0.1000   -0.0008
   260        0.7347             nan     0.1000   -0.0009
   280        0.7323             nan     0.1000   -0.0009
   300        0.7277             nan     0.1000   -0.0009
   320        0.7243             nan     0.1000   -0.0013
   340        0.7207             nan     0.1000   -0.0008
   360        0.7164             nan     0.1000   -0.0005
   380        0.7130             nan     0.1000   -0.0002
   400        0.7104             nan     0.1000   -0.0007
   420        0.7076             nan     0.1000   -0.0007
   440        0.7050             nan     0.1000   -0.0008
   460        0.7019             nan     0.1000   -0.0010
   480        0.7003             nan     0.1000   -0.0012
   500        0.6978             nan     0.1000   -0.0006
   520        0.6955             nan     0.1000   -0.0013
   540        0.6934             nan     0.1000   -0.0005
   560        0.6917             nan     0.1000   -0.0013
   580        0.6894             nan     0.1000   -0.0009
   600        0.6870             nan     0.1000   -0.0010
   620        0.6850             nan     0.1000   -0.0008
   640        0.6830             nan     0.1000   -0.0007
   660        0.6818             nan     0.1000   -0.0006
   680        0.6805             nan     0.1000   -0.0011
   700        0.6777             nan     0.1000   -0.0005
   720        0.6768             nan     0.1000   -0.0007
   740        0.6754             nan     0.1000   -0.0014
   760        0.6728             nan     0.1000   -0.0003
   780        0.6718             nan     0.1000   -0.0012
   800        0.6707             nan     0.1000   -0.0008
   820        0.6694             nan     0.1000   -0.0003
   840        0.6677             nan     0.1000   -0.0015
   860        0.6643             nan     0.1000   -0.0006
   880        0.6623             nan     0.1000   -0.0002
   900        0.6617             nan     0.1000   -0.0003
   920        0.6594             nan     0.1000   -0.0006
   940        0.6577             nan     0.1000   -0.0007
   960        0.6574             nan     0.1000   -0.0013
   980        0.6566             nan     0.1000   -0.0014
  1000        0.6554             nan     0.1000   -0.0004
  1020        0.6546             nan     0.1000   -0.0005
  1040        0.6519             nan     0.1000   -0.0006
  1060        0.6509             nan     0.1000   -0.0005
  1080        0.6498             nan     0.1000   -0.0012
  1100        0.6469             nan     0.1000   -0.0014

- Fold07.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2639             nan     0.1000    0.0328
     2        1.2031             nan     0.1000    0.0273
     3        1.1577             nan     0.1000    0.0224
     4        1.1174             nan     0.1000    0.0214
     5        1.0848             nan     0.1000    0.0165
     6        1.0563             nan     0.1000    0.0130
     7        1.0300             nan     0.1000    0.0116
     8        1.0069             nan     0.1000    0.0105
     9        0.9879             nan     0.1000    0.0082
    10        0.9703             nan     0.1000    0.0065
    20        0.8722             nan     0.1000    0.0023
    40        0.7994             nan     0.1000   -0.0013
    60        0.7616             nan     0.1000   -0.0008
    80        0.7372             nan     0.1000   -0.0012
   100        0.7156             nan     0.1000   -0.0013
   120        0.6960             nan     0.1000   -0.0013
   140        0.6820             nan     0.1000   -0.0021
   160        0.6721             nan     0.1000   -0.0010
   180        0.6575             nan     0.1000   -0.0007
   200        0.6456             nan     0.1000   -0.0010
   220        0.6335             nan     0.1000   -0.0006
   240        0.6216             nan     0.1000   -0.0008
   260        0.6118             nan     0.1000   -0.0011
   280        0.6005             nan     0.1000   -0.0015
   300        0.5918             nan     0.1000   -0.0018
   320        0.5827             nan     0.1000   -0.0011
   340        0.5744             nan     0.1000   -0.0008
   360        0.5685             nan     0.1000   -0.0009
   380        0.5605             nan     0.1000   -0.0011
   400        0.5521             nan     0.1000   -0.0008
   420        0.5454             nan     0.1000   -0.0013
   440        0.5393             nan     0.1000   -0.0008
   460        0.5336             nan     0.1000   -0.0008
   480        0.5284             nan     0.1000   -0.0009
   500        0.5231             nan     0.1000   -0.0008
   520        0.5170             nan     0.1000   -0.0005
   540        0.5110             nan     0.1000   -0.0009
   560        0.5052             nan     0.1000   -0.0017
   580        0.5000             nan     0.1000   -0.0007
   600        0.4940             nan     0.1000   -0.0009
   620        0.4907             nan     0.1000   -0.0015
   640        0.4841             nan     0.1000   -0.0007
   660        0.4792             nan     0.1000   -0.0016
   680        0.4738             nan     0.1000   -0.0007
   700        0.4704             nan     0.1000   -0.0015
   720        0.4660             nan     0.1000   -0.0008
   740        0.4613             nan     0.1000   -0.0010
   760        0.4596             nan     0.1000   -0.0011
   780        0.4563             nan     0.1000   -0.0011
   800        0.4521             nan     0.1000   -0.0013
   820        0.4475             nan     0.1000   -0.0013
   840        0.4448             nan     0.1000   -0.0015
   860        0.4406             nan     0.1000   -0.0005
   880        0.4373             nan     0.1000   -0.0017
   900        0.4329             nan     0.1000   -0.0006
   920        0.4275             nan     0.1000   -0.0009
   940        0.4230             nan     0.1000   -0.0005
   960        0.4198             nan     0.1000   -0.0004
   980        0.4170             nan     0.1000   -0.0011
  1000        0.4138             nan     0.1000   -0.0006
  1020        0.4106             nan     0.1000   -0.0007
  1040        0.4065             nan     0.1000   -0.0006
  1060        0.4037             nan     0.1000   -0.0006
  1080        0.4002             nan     0.1000   -0.0010
  1100        0.3966             nan     0.1000   -0.0009

- Fold07.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2553             nan     0.1000    0.0371
     2        1.1903             nan     0.1000    0.0286
     3        1.1397             nan     0.1000    0.0244
     4        1.0965             nan     0.1000    0.0208
     5        1.0558             nan     0.1000    0.0165
     6        1.0224             nan     0.1000    0.0140
     7        0.9955             nan     0.1000    0.0135
     8        0.9717             nan     0.1000    0.0096
     9        0.9508             nan     0.1000    0.0083
    10        0.9332             nan     0.1000    0.0061
    20        0.8415             nan     0.1000    0.0018
    40        0.7701             nan     0.1000    0.0002
    60        0.7185             nan     0.1000   -0.0005
    80        0.6895             nan     0.1000   -0.0011
   100        0.6598             nan     0.1000   -0.0010
   120        0.6380             nan     0.1000   -0.0014
   140        0.6212             nan     0.1000   -0.0005
   160        0.5986             nan     0.1000   -0.0018
   180        0.5827             nan     0.1000   -0.0021
   200        0.5700             nan     0.1000   -0.0018
   220        0.5533             nan     0.1000   -0.0013
   240        0.5406             nan     0.1000   -0.0009
   260        0.5259             nan     0.1000   -0.0009
   280        0.5133             nan     0.1000   -0.0016
   300        0.5033             nan     0.1000   -0.0017
   320        0.4929             nan     0.1000   -0.0007
   340        0.4821             nan     0.1000   -0.0012
   360        0.4732             nan     0.1000   -0.0017
   380        0.4646             nan     0.1000   -0.0011
   400        0.4543             nan     0.1000   -0.0007
   420        0.4435             nan     0.1000   -0.0017
   440        0.4329             nan     0.1000   -0.0008
   460        0.4247             nan     0.1000   -0.0005
   480        0.4164             nan     0.1000   -0.0009
   500        0.4085             nan     0.1000   -0.0016
   520        0.4028             nan     0.1000   -0.0007
   540        0.3968             nan     0.1000   -0.0010
   560        0.3899             nan     0.1000   -0.0007
   580        0.3843             nan     0.1000   -0.0013
   600        0.3785             nan     0.1000   -0.0010
   620        0.3722             nan     0.1000   -0.0004
   640        0.3665             nan     0.1000   -0.0005
   660        0.3595             nan     0.1000   -0.0005
   680        0.3552             nan     0.1000   -0.0007
   700        0.3487             nan     0.1000   -0.0006
   720        0.3444             nan     0.1000   -0.0009
   740        0.3398             nan     0.1000   -0.0012
   760        0.3362             nan     0.1000   -0.0014
   780        0.3312             nan     0.1000   -0.0012
   800        0.3267             nan     0.1000   -0.0004
   820        0.3230             nan     0.1000   -0.0010
   840        0.3190             nan     0.1000   -0.0010
   860        0.3157             nan     0.1000   -0.0007
   880        0.3106             nan     0.1000   -0.0009
   900        0.3066             nan     0.1000   -0.0009
   920        0.3009             nan     0.1000   -0.0007
   940        0.2974             nan     0.1000   -0.0006
   960        0.2936             nan     0.1000   -0.0004
   980        0.2895             nan     0.1000   -0.0005
  1000        0.2859             nan     0.1000   -0.0013
  1020        0.2804             nan     0.1000   -0.0006
  1040        0.2766             nan     0.1000   -0.0009
  1060        0.2743             nan     0.1000   -0.0014
  1080        0.2717             nan     0.1000   -0.0007
  1100        0.2679             nan     0.1000   -0.0005

- Fold07.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3263             nan     0.0100    0.0028
     2        1.3209             nan     0.0100    0.0028
     3        1.3154             nan     0.0100    0.0028
     4        1.3103             nan     0.0100    0.0027
     5        1.3047             nan     0.0100    0.0027
     6        1.2995             nan     0.0100    0.0026
     7        1.2939             nan     0.0100    0.0026
     8        1.2884             nan     0.0100    0.0025
     9        1.2835             nan     0.0100    0.0025
    10        1.2785             nan     0.0100    0.0024
    20        1.2349             nan     0.0100    0.0020
    40        1.1678             nan     0.0100    0.0015
    60        1.1204             nan     0.0100    0.0009
    80        1.0830             nan     0.0100    0.0008
   100        1.0527             nan     0.0100    0.0007
   120        1.0272             nan     0.0100    0.0005
   140        1.0065             nan     0.0100    0.0004
   160        0.9882             nan     0.0100    0.0003
   180        0.9722             nan     0.0100    0.0003
   200        0.9592             nan     0.0100    0.0002
   220        0.9471             nan     0.0100    0.0002
   240        0.9362             nan     0.0100    0.0002
   260        0.9253             nan     0.0100    0.0002
   280        0.9164             nan     0.0100    0.0001
   300        0.9087             nan     0.0100    0.0000
   320        0.9018             nan     0.0100    0.0001
   340        0.8954             nan     0.0100    0.0002
   360        0.8890             nan     0.0100    0.0000
   380        0.8834             nan     0.0100    0.0001
   400        0.8782             nan     0.0100   -0.0000
   420        0.8733             nan     0.0100    0.0001
   440        0.8685             nan     0.0100    0.0001
   460        0.8638             nan     0.0100    0.0001
   480        0.8594             nan     0.0100    0.0001
   500        0.8555             nan     0.0100    0.0001
   520        0.8517             nan     0.0100    0.0000
   540        0.8481             nan     0.0100   -0.0000
   560        0.8450             nan     0.0100   -0.0000
   580        0.8419             nan     0.0100   -0.0000
   600        0.8391             nan     0.0100   -0.0001
   620        0.8360             nan     0.0100    0.0001
   640        0.8333             nan     0.0100   -0.0001
   660        0.8308             nan     0.0100   -0.0001
   680        0.8282             nan     0.0100   -0.0001
   700        0.8255             nan     0.0100    0.0000
   720        0.8231             nan     0.0100   -0.0000
   740        0.8207             nan     0.0100    0.0000
   760        0.8183             nan     0.0100   -0.0000
   780        0.8159             nan     0.0100   -0.0000
   800        0.8138             nan     0.0100   -0.0001
   820        0.8118             nan     0.0100   -0.0000
   840        0.8100             nan     0.0100   -0.0001
   860        0.8078             nan     0.0100   -0.0001
   880        0.8059             nan     0.0100    0.0000
   900        0.8038             nan     0.0100   -0.0000
   920        0.8020             nan     0.0100    0.0000
   940        0.8004             nan     0.0100   -0.0001
   960        0.7990             nan     0.0100   -0.0000
   980        0.7976             nan     0.0100   -0.0000
  1000        0.7959             nan     0.0100   -0.0000
  1020        0.7946             nan     0.0100   -0.0001
  1040        0.7931             nan     0.0100   -0.0000
  1060        0.7914             nan     0.0100    0.0000
  1080        0.7902             nan     0.0100   -0.0001
  1100        0.7887             nan     0.0100   -0.0001

- Fold08.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3237             nan     0.0100    0.0038
     2        1.3163             nan     0.0100    0.0036
     3        1.3090             nan     0.0100    0.0034
     4        1.3022             nan     0.0100    0.0033
     5        1.2949             nan     0.0100    0.0031
     6        1.2882             nan     0.0100    0.0033
     7        1.2815             nan     0.0100    0.0030
     8        1.2747             nan     0.0100    0.0031
     9        1.2685             nan     0.0100    0.0030
    10        1.2621             nan     0.0100    0.0030
    20        1.2067             nan     0.0100    0.0025
    40        1.1192             nan     0.0100    0.0017
    60        1.0554             nan     0.0100    0.0013
    80        1.0069             nan     0.0100    0.0010
   100        0.9696             nan     0.0100    0.0006
   120        0.9414             nan     0.0100    0.0005
   140        0.9185             nan     0.0100    0.0005
   160        0.9001             nan     0.0100    0.0004
   180        0.8840             nan     0.0100    0.0002
   200        0.8692             nan     0.0100    0.0002
   220        0.8572             nan     0.0100    0.0002
   240        0.8466             nan     0.0100    0.0000
   260        0.8376             nan     0.0100    0.0001
   280        0.8294             nan     0.0100    0.0001
   300        0.8215             nan     0.0100    0.0001
   320        0.8146             nan     0.0100   -0.0000
   340        0.8083             nan     0.0100   -0.0001
   360        0.8025             nan     0.0100   -0.0000
   380        0.7968             nan     0.0100   -0.0000
   400        0.7918             nan     0.0100   -0.0000
   420        0.7871             nan     0.0100   -0.0000
   440        0.7825             nan     0.0100   -0.0001
   460        0.7787             nan     0.0100   -0.0001
   480        0.7748             nan     0.0100   -0.0000
   500        0.7710             nan     0.0100   -0.0000
   520        0.7673             nan     0.0100    0.0000
   540        0.7642             nan     0.0100   -0.0001
   560        0.7607             nan     0.0100   -0.0001
   580        0.7571             nan     0.0100   -0.0001
   600        0.7533             nan     0.0100   -0.0001
   620        0.7504             nan     0.0100   -0.0000
   640        0.7477             nan     0.0100   -0.0001
   660        0.7450             nan     0.0100   -0.0000
   680        0.7424             nan     0.0100   -0.0001
   700        0.7400             nan     0.0100   -0.0000
   720        0.7376             nan     0.0100   -0.0001
   740        0.7350             nan     0.0100   -0.0000
   760        0.7325             nan     0.0100   -0.0001
   780        0.7298             nan     0.0100   -0.0001
   800        0.7275             nan     0.0100   -0.0001
   820        0.7252             nan     0.0100   -0.0000
   840        0.7229             nan     0.0100   -0.0000
   860        0.7208             nan     0.0100   -0.0001
   880        0.7182             nan     0.0100   -0.0001
   900        0.7163             nan     0.0100   -0.0001
   920        0.7139             nan     0.0100   -0.0001
   940        0.7119             nan     0.0100   -0.0001
   960        0.7098             nan     0.0100   -0.0002
   980        0.7081             nan     0.0100   -0.0000
  1000        0.7064             nan     0.0100   -0.0001
  1020        0.7041             nan     0.0100    0.0000
  1040        0.7020             nan     0.0100   -0.0000
  1060        0.6999             nan     0.0100   -0.0001
  1080        0.6982             nan     0.0100   -0.0000
  1100        0.6964             nan     0.0100   -0.0002

- Fold08.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0042
     2        1.3152             nan     0.0100    0.0039
     3        1.3070             nan     0.0100    0.0036
     4        1.2991             nan     0.0100    0.0038
     5        1.2917             nan     0.0100    0.0037
     6        1.2843             nan     0.0100    0.0034
     7        1.2772             nan     0.0100    0.0036
     8        1.2709             nan     0.0100    0.0031
     9        1.2638             nan     0.0100    0.0034
    10        1.2569             nan     0.0100    0.0035
    20        1.1935             nan     0.0100    0.0028
    40        1.0962             nan     0.0100    0.0020
    60        1.0253             nan     0.0100    0.0015
    80        0.9726             nan     0.0100    0.0011
   100        0.9316             nan     0.0100    0.0006
   120        0.9019             nan     0.0100    0.0003
   140        0.8775             nan     0.0100    0.0004
   160        0.8585             nan     0.0100    0.0003
   180        0.8429             nan     0.0100   -0.0000
   200        0.8284             nan     0.0100    0.0001
   220        0.8166             nan     0.0100    0.0001
   240        0.8057             nan     0.0100    0.0001
   260        0.7961             nan     0.0100    0.0001
   280        0.7872             nan     0.0100    0.0001
   300        0.7800             nan     0.0100   -0.0000
   320        0.7731             nan     0.0100   -0.0002
   340        0.7667             nan     0.0100    0.0001
   360        0.7607             nan     0.0100    0.0001
   380        0.7551             nan     0.0100   -0.0001
   400        0.7495             nan     0.0100   -0.0000
   420        0.7442             nan     0.0100    0.0000
   440        0.7395             nan     0.0100   -0.0001
   460        0.7348             nan     0.0100   -0.0001
   480        0.7300             nan     0.0100   -0.0002
   500        0.7253             nan     0.0100   -0.0001
   520        0.7204             nan     0.0100   -0.0001
   540        0.7159             nan     0.0100   -0.0000
   560        0.7118             nan     0.0100   -0.0000
   580        0.7077             nan     0.0100   -0.0001
   600        0.7039             nan     0.0100   -0.0001
   620        0.6998             nan     0.0100   -0.0001
   640        0.6961             nan     0.0100   -0.0001
   660        0.6927             nan     0.0100   -0.0002
   680        0.6888             nan     0.0100   -0.0000
   700        0.6857             nan     0.0100    0.0000
   720        0.6822             nan     0.0100   -0.0001
   740        0.6796             nan     0.0100   -0.0001
   760        0.6764             nan     0.0100   -0.0001
   780        0.6734             nan     0.0100   -0.0001
   800        0.6706             nan     0.0100   -0.0002
   820        0.6674             nan     0.0100   -0.0001
   840        0.6646             nan     0.0100   -0.0001
   860        0.6619             nan     0.0100   -0.0001
   880        0.6589             nan     0.0100   -0.0001
   900        0.6562             nan     0.0100    0.0000
   920        0.6537             nan     0.0100   -0.0001
   940        0.6509             nan     0.0100   -0.0000
   960        0.6483             nan     0.0100   -0.0002
   980        0.6458             nan     0.0100    0.0000
  1000        0.6437             nan     0.0100   -0.0001
  1020        0.6411             nan     0.0100   -0.0002
  1040        0.6387             nan     0.0100   -0.0001
  1060        0.6364             nan     0.0100   -0.0001
  1080        0.6339             nan     0.0100   -0.0001
  1100        0.6318             nan     0.0100   -0.0001

- Fold08.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2717             nan     0.1000    0.0265
     2        1.2279             nan     0.1000    0.0228
     3        1.1927             nan     0.1000    0.0179
     4        1.1635             nan     0.1000    0.0159
     5        1.1423             nan     0.1000    0.0073
     6        1.1176             nan     0.1000    0.0125
     7        1.0978             nan     0.1000    0.0101
     8        1.0810             nan     0.1000    0.0086
     9        1.0664             nan     0.1000    0.0058
    10        1.0528             nan     0.1000    0.0071
    20        0.9547             nan     0.1000    0.0035
    40        0.8788             nan     0.1000    0.0006
    60        0.8395             nan     0.1000   -0.0003
    80        0.8164             nan     0.1000   -0.0000
   100        0.7980             nan     0.1000   -0.0004
   120        0.7838             nan     0.1000    0.0001
   140        0.7739             nan     0.1000   -0.0009
   160        0.7656             nan     0.1000   -0.0005
   180        0.7580             nan     0.1000   -0.0008
   200        0.7523             nan     0.1000   -0.0006
   220        0.7462             nan     0.1000   -0.0008
   240        0.7403             nan     0.1000   -0.0011
   260        0.7364             nan     0.1000   -0.0013
   280        0.7314             nan     0.1000   -0.0004
   300        0.7292             nan     0.1000   -0.0018
   320        0.7242             nan     0.1000   -0.0002
   340        0.7211             nan     0.1000   -0.0006
   360        0.7190             nan     0.1000   -0.0006
   380        0.7140             nan     0.1000   -0.0001
   400        0.7100             nan     0.1000   -0.0015
   420        0.7075             nan     0.1000   -0.0004
   440        0.7039             nan     0.1000   -0.0006
   460        0.7012             nan     0.1000   -0.0008
   480        0.6983             nan     0.1000   -0.0006
   500        0.6950             nan     0.1000   -0.0015
   520        0.6930             nan     0.1000   -0.0012
   540        0.6909             nan     0.1000   -0.0010
   560        0.6876             nan     0.1000   -0.0006
   580        0.6872             nan     0.1000   -0.0007
   600        0.6837             nan     0.1000   -0.0010
   620        0.6812             nan     0.1000   -0.0004
   640        0.6788             nan     0.1000   -0.0010
   660        0.6766             nan     0.1000   -0.0007
   680        0.6761             nan     0.1000   -0.0003
   700        0.6738             nan     0.1000   -0.0011
   720        0.6716             nan     0.1000   -0.0005
   740        0.6697             nan     0.1000   -0.0007
   760        0.6685             nan     0.1000   -0.0014
   780        0.6671             nan     0.1000   -0.0010
   800        0.6650             nan     0.1000   -0.0002
   820        0.6634             nan     0.1000   -0.0008
   840        0.6625             nan     0.1000   -0.0002
   860        0.6607             nan     0.1000   -0.0007
   880        0.6585             nan     0.1000   -0.0011
   900        0.6562             nan     0.1000   -0.0009
   920        0.6551             nan     0.1000   -0.0008
   940        0.6533             nan     0.1000   -0.0015
   960        0.6516             nan     0.1000   -0.0014
   980        0.6498             nan     0.1000   -0.0006
  1000        0.6484             nan     0.1000   -0.0010
  1020        0.6468             nan     0.1000   -0.0012
  1040        0.6465             nan     0.1000   -0.0010
  1060        0.6449             nan     0.1000   -0.0006
  1080        0.6436             nan     0.1000   -0.0008
  1100        0.6423             nan     0.1000   -0.0008

- Fold08.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2611             nan     0.1000    0.0350
     2        1.2077             nan     0.1000    0.0236
     3        1.1587             nan     0.1000    0.0258
     4        1.1198             nan     0.1000    0.0206
     5        1.0859             nan     0.1000    0.0184
     6        1.0561             nan     0.1000    0.0134
     7        1.0338             nan     0.1000    0.0108
     8        1.0096             nan     0.1000    0.0122
     9        0.9869             nan     0.1000    0.0096
    10        0.9688             nan     0.1000    0.0075
    20        0.8694             nan     0.1000    0.0003
    40        0.7938             nan     0.1000   -0.0001
    60        0.7523             nan     0.1000   -0.0006
    80        0.7283             nan     0.1000   -0.0013
   100        0.7078             nan     0.1000   -0.0024
   120        0.6891             nan     0.1000   -0.0008
   140        0.6734             nan     0.1000   -0.0013
   160        0.6625             nan     0.1000   -0.0012
   180        0.6525             nan     0.1000   -0.0007
   200        0.6386             nan     0.1000   -0.0007
   220        0.6279             nan     0.1000   -0.0012
   240        0.6158             nan     0.1000   -0.0004
   260        0.6029             nan     0.1000   -0.0011
   280        0.5928             nan     0.1000   -0.0015
   300        0.5839             nan     0.1000   -0.0008
   320        0.5746             nan     0.1000   -0.0018
   340        0.5668             nan     0.1000   -0.0004
   360        0.5586             nan     0.1000   -0.0011
   380        0.5520             nan     0.1000   -0.0009
   400        0.5449             nan     0.1000   -0.0011
   420        0.5366             nan     0.1000   -0.0018
   440        0.5306             nan     0.1000   -0.0009
   460        0.5243             nan     0.1000   -0.0007
   480        0.5194             nan     0.1000   -0.0027
   500        0.5133             nan     0.1000   -0.0012
   520        0.5072             nan     0.1000   -0.0021
   540        0.5039             nan     0.1000   -0.0014
   560        0.4974             nan     0.1000   -0.0007
   580        0.4937             nan     0.1000   -0.0014
   600        0.4875             nan     0.1000   -0.0008
   620        0.4818             nan     0.1000   -0.0013
   640        0.4778             nan     0.1000   -0.0012
   660        0.4714             nan     0.1000   -0.0009
   680        0.4665             nan     0.1000   -0.0003
   700        0.4618             nan     0.1000   -0.0010
   720        0.4574             nan     0.1000   -0.0003
   740        0.4544             nan     0.1000   -0.0007
   760        0.4491             nan     0.1000   -0.0010
   780        0.4444             nan     0.1000   -0.0004
   800        0.4406             nan     0.1000   -0.0009
   820        0.4377             nan     0.1000   -0.0006
   840        0.4338             nan     0.1000   -0.0005
   860        0.4291             nan     0.1000   -0.0006
   880        0.4271             nan     0.1000   -0.0018
   900        0.4235             nan     0.1000   -0.0010
   920        0.4211             nan     0.1000   -0.0009
   940        0.4167             nan     0.1000   -0.0003
   960        0.4144             nan     0.1000   -0.0010
   980        0.4113             nan     0.1000   -0.0012
  1000        0.4056             nan     0.1000   -0.0003
  1020        0.4011             nan     0.1000   -0.0011
  1040        0.3989             nan     0.1000   -0.0007
  1060        0.3951             nan     0.1000   -0.0004
  1080        0.3919             nan     0.1000   -0.0005
  1100        0.3884             nan     0.1000   -0.0003

- Fold08.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2543             nan     0.1000    0.0362
     2        1.1909             nan     0.1000    0.0294
     3        1.1409             nan     0.1000    0.0188
     4        1.0918             nan     0.1000    0.0218
     5        1.0557             nan     0.1000    0.0159
     6        1.0207             nan     0.1000    0.0166
     7        0.9916             nan     0.1000    0.0115
     8        0.9686             nan     0.1000    0.0095
     9        0.9456             nan     0.1000    0.0099
    10        0.9298             nan     0.1000    0.0059
    20        0.8246             nan     0.1000    0.0015
    40        0.7446             nan     0.1000   -0.0013
    60        0.7018             nan     0.1000   -0.0012
    80        0.6685             nan     0.1000   -0.0011
   100        0.6402             nan     0.1000   -0.0020
   120        0.6193             nan     0.1000   -0.0016
   140        0.6002             nan     0.1000   -0.0012
   160        0.5824             nan     0.1000   -0.0011
   180        0.5651             nan     0.1000   -0.0011
   200        0.5527             nan     0.1000   -0.0007
   220        0.5379             nan     0.1000   -0.0013
   240        0.5242             nan     0.1000   -0.0010
   260        0.5122             nan     0.1000   -0.0009
   280        0.5005             nan     0.1000   -0.0010
   300        0.4884             nan     0.1000   -0.0024
   320        0.4756             nan     0.1000   -0.0011
   340        0.4658             nan     0.1000   -0.0006
   360        0.4584             nan     0.1000   -0.0009
   380        0.4504             nan     0.1000   -0.0009
   400        0.4398             nan     0.1000   -0.0016
   420        0.4314             nan     0.1000   -0.0010
   440        0.4210             nan     0.1000   -0.0004
   460        0.4120             nan     0.1000   -0.0008
   480        0.4029             nan     0.1000   -0.0012
   500        0.3957             nan     0.1000   -0.0014
   520        0.3891             nan     0.1000   -0.0012
   540        0.3801             nan     0.1000   -0.0010
   560        0.3733             nan     0.1000   -0.0013
   580        0.3663             nan     0.1000   -0.0013
   600        0.3603             nan     0.1000   -0.0010
   620        0.3556             nan     0.1000   -0.0010
   640        0.3493             nan     0.1000   -0.0014
   660        0.3433             nan     0.1000   -0.0008
   680        0.3374             nan     0.1000   -0.0014
   700        0.3307             nan     0.1000   -0.0005
   720        0.3254             nan     0.1000   -0.0010
   740        0.3213             nan     0.1000   -0.0011
   760        0.3160             nan     0.1000   -0.0009
   780        0.3111             nan     0.1000   -0.0007
   800        0.3057             nan     0.1000   -0.0006
   820        0.3017             nan     0.1000   -0.0016
   840        0.2954             nan     0.1000   -0.0009
   860        0.2898             nan     0.1000   -0.0006
   880        0.2855             nan     0.1000   -0.0005
   900        0.2811             nan     0.1000   -0.0005
   920        0.2773             nan     0.1000   -0.0013
   940        0.2744             nan     0.1000   -0.0011
   960        0.2708             nan     0.1000   -0.0007
   980        0.2676             nan     0.1000   -0.0011
  1000        0.2629             nan     0.1000   -0.0008
  1020        0.2589             nan     0.1000   -0.0008
  1040        0.2554             nan     0.1000   -0.0005
  1060        0.2516             nan     0.1000   -0.0003
  1080        0.2487             nan     0.1000   -0.0003
  1100        0.2455             nan     0.1000   -0.0005

- Fold08.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3253             nan     0.0100    0.0030
     2        1.3197             nan     0.0100    0.0028
     3        1.3136             nan     0.0100    0.0028
     4        1.3083             nan     0.0100    0.0028
     5        1.3032             nan     0.0100    0.0026
     6        1.2978             nan     0.0100    0.0026
     7        1.2925             nan     0.0100    0.0027
     8        1.2874             nan     0.0100    0.0026
     9        1.2822             nan     0.0100    0.0027
    10        1.2767             nan     0.0100    0.0025
    20        1.2314             nan     0.0100    0.0021
    40        1.1622             nan     0.0100    0.0014
    60        1.1122             nan     0.0100    0.0008
    80        1.0742             nan     0.0100    0.0007
   100        1.0445             nan     0.0100    0.0006
   120        1.0211             nan     0.0100    0.0006
   140        1.0001             nan     0.0100    0.0004
   160        0.9820             nan     0.0100    0.0001
   180        0.9665             nan     0.0100    0.0003
   200        0.9522             nan     0.0100    0.0002
   220        0.9396             nan     0.0100    0.0003
   240        0.9285             nan     0.0100    0.0003
   260        0.9189             nan     0.0100    0.0003
   280        0.9097             nan     0.0100   -0.0000
   300        0.9021             nan     0.0100    0.0001
   320        0.8948             nan     0.0100    0.0001
   340        0.8881             nan     0.0100    0.0001
   360        0.8817             nan     0.0100    0.0000
   380        0.8761             nan     0.0100    0.0001
   400        0.8710             nan     0.0100   -0.0000
   420        0.8660             nan     0.0100    0.0000
   440        0.8612             nan     0.0100    0.0001
   460        0.8565             nan     0.0100    0.0001
   480        0.8522             nan     0.0100    0.0000
   500        0.8479             nan     0.0100    0.0001
   520        0.8441             nan     0.0100    0.0000
   540        0.8406             nan     0.0100   -0.0000
   560        0.8371             nan     0.0100    0.0000
   580        0.8338             nan     0.0100    0.0000
   600        0.8305             nan     0.0100   -0.0000
   620        0.8276             nan     0.0100   -0.0000
   640        0.8249             nan     0.0100   -0.0000
   660        0.8224             nan     0.0100   -0.0000
   680        0.8196             nan     0.0100   -0.0000
   700        0.8173             nan     0.0100   -0.0000
   720        0.8150             nan     0.0100   -0.0001
   740        0.8126             nan     0.0100   -0.0001
   760        0.8102             nan     0.0100   -0.0000
   780        0.8079             nan     0.0100   -0.0000
   800        0.8061             nan     0.0100   -0.0000
   820        0.8038             nan     0.0100   -0.0000
   840        0.8018             nan     0.0100   -0.0000
   860        0.7998             nan     0.0100   -0.0001
   880        0.7978             nan     0.0100   -0.0000
   900        0.7962             nan     0.0100   -0.0000
   920        0.7946             nan     0.0100   -0.0000
   940        0.7927             nan     0.0100   -0.0001
   960        0.7910             nan     0.0100    0.0000
   980        0.7893             nan     0.0100   -0.0000
  1000        0.7878             nan     0.0100   -0.0000
  1020        0.7866             nan     0.0100   -0.0001
  1040        0.7854             nan     0.0100   -0.0000
  1060        0.7838             nan     0.0100   -0.0000
  1080        0.7826             nan     0.0100   -0.0000
  1100        0.7812             nan     0.0100   -0.0001

- Fold09.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0035
     2        1.3173             nan     0.0100    0.0034
     3        1.3101             nan     0.0100    0.0035
     4        1.3036             nan     0.0100    0.0033
     5        1.2964             nan     0.0100    0.0034
     6        1.2894             nan     0.0100    0.0035
     7        1.2825             nan     0.0100    0.0031
     8        1.2759             nan     0.0100    0.0031
     9        1.2700             nan     0.0100    0.0031
    10        1.2636             nan     0.0100    0.0030
    20        1.2065             nan     0.0100    0.0025
    40        1.1163             nan     0.0100    0.0017
    60        1.0512             nan     0.0100    0.0013
    80        1.0029             nan     0.0100    0.0010
   100        0.9651             nan     0.0100    0.0006
   120        0.9356             nan     0.0100    0.0006
   140        0.9127             nan     0.0100    0.0005
   160        0.8936             nan     0.0100    0.0003
   180        0.8784             nan     0.0100    0.0003
   200        0.8650             nan     0.0100    0.0002
   220        0.8529             nan     0.0100    0.0002
   240        0.8428             nan     0.0100    0.0001
   260        0.8338             nan     0.0100    0.0001
   280        0.8255             nan     0.0100    0.0001
   300        0.8182             nan     0.0100    0.0000
   320        0.8115             nan     0.0100   -0.0001
   340        0.8050             nan     0.0100    0.0000
   360        0.7994             nan     0.0100    0.0001
   380        0.7935             nan     0.0100    0.0000
   400        0.7887             nan     0.0100   -0.0001
   420        0.7838             nan     0.0100    0.0000
   440        0.7792             nan     0.0100   -0.0001
   460        0.7749             nan     0.0100   -0.0000
   480        0.7714             nan     0.0100   -0.0000
   500        0.7675             nan     0.0100   -0.0001
   520        0.7638             nan     0.0100   -0.0000
   540        0.7602             nan     0.0100   -0.0001
   560        0.7569             nan     0.0100   -0.0001
   580        0.7539             nan     0.0100   -0.0000
   600        0.7509             nan     0.0100   -0.0002
   620        0.7479             nan     0.0100   -0.0002
   640        0.7447             nan     0.0100    0.0001
   660        0.7418             nan     0.0100   -0.0001
   680        0.7389             nan     0.0100   -0.0001
   700        0.7362             nan     0.0100    0.0000
   720        0.7334             nan     0.0100   -0.0001
   740        0.7306             nan     0.0100   -0.0001
   760        0.7279             nan     0.0100   -0.0001
   780        0.7257             nan     0.0100   -0.0001
   800        0.7232             nan     0.0100   -0.0001
   820        0.7211             nan     0.0100    0.0000
   840        0.7190             nan     0.0100   -0.0001
   860        0.7167             nan     0.0100   -0.0001
   880        0.7148             nan     0.0100   -0.0001
   900        0.7128             nan     0.0100   -0.0001
   920        0.7109             nan     0.0100   -0.0000
   940        0.7087             nan     0.0100   -0.0000
   960        0.7068             nan     0.0100   -0.0001
   980        0.7049             nan     0.0100   -0.0001
  1000        0.7028             nan     0.0100   -0.0000
  1020        0.7009             nan     0.0100   -0.0000
  1040        0.6992             nan     0.0100   -0.0000
  1060        0.6974             nan     0.0100   -0.0001
  1080        0.6956             nan     0.0100   -0.0000
  1100        0.6936             nan     0.0100   -0.0001

- Fold09.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3231             nan     0.0100    0.0040
     2        1.3153             nan     0.0100    0.0039
     3        1.3070             nan     0.0100    0.0040
     4        1.2991             nan     0.0100    0.0038
     5        1.2915             nan     0.0100    0.0035
     6        1.2836             nan     0.0100    0.0035
     7        1.2756             nan     0.0100    0.0034
     8        1.2686             nan     0.0100    0.0036
     9        1.2618             nan     0.0100    0.0031
    10        1.2547             nan     0.0100    0.0032
    20        1.1930             nan     0.0100    0.0028
    40        1.0951             nan     0.0100    0.0019
    60        1.0257             nan     0.0100    0.0014
    80        0.9739             nan     0.0100    0.0011
   100        0.9331             nan     0.0100    0.0007
   120        0.9018             nan     0.0100    0.0006
   140        0.8781             nan     0.0100    0.0003
   160        0.8588             nan     0.0100    0.0002
   180        0.8433             nan     0.0100    0.0002
   200        0.8297             nan     0.0100    0.0001
   220        0.8170             nan     0.0100    0.0001
   240        0.8066             nan     0.0100    0.0001
   260        0.7963             nan     0.0100    0.0001
   280        0.7879             nan     0.0100    0.0001
   300        0.7807             nan     0.0100    0.0000
   320        0.7735             nan     0.0100   -0.0000
   340        0.7662             nan     0.0100    0.0000
   360        0.7595             nan     0.0100   -0.0000
   380        0.7537             nan     0.0100    0.0000
   400        0.7478             nan     0.0100   -0.0000
   420        0.7427             nan     0.0100    0.0000
   440        0.7380             nan     0.0100   -0.0000
   460        0.7329             nan     0.0100   -0.0001
   480        0.7285             nan     0.0100   -0.0001
   500        0.7241             nan     0.0100   -0.0000
   520        0.7197             nan     0.0100   -0.0000
   540        0.7154             nan     0.0100   -0.0001
   560        0.7111             nan     0.0100   -0.0002
   580        0.7073             nan     0.0100   -0.0001
   600        0.7035             nan     0.0100   -0.0001
   620        0.6994             nan     0.0100   -0.0001
   640        0.6951             nan     0.0100   -0.0001
   660        0.6914             nan     0.0100   -0.0002
   680        0.6880             nan     0.0100   -0.0000
   700        0.6852             nan     0.0100   -0.0002
   720        0.6814             nan     0.0100   -0.0001
   740        0.6782             nan     0.0100   -0.0001
   760        0.6752             nan     0.0100   -0.0001
   780        0.6720             nan     0.0100   -0.0001
   800        0.6681             nan     0.0100   -0.0001
   820        0.6651             nan     0.0100   -0.0002
   840        0.6620             nan     0.0100   -0.0002
   860        0.6593             nan     0.0100   -0.0003
   880        0.6567             nan     0.0100   -0.0000
   900        0.6535             nan     0.0100   -0.0001
   920        0.6509             nan     0.0100    0.0000
   940        0.6484             nan     0.0100   -0.0001
   960        0.6457             nan     0.0100   -0.0001
   980        0.6429             nan     0.0100   -0.0001
  1000        0.6405             nan     0.0100   -0.0001
  1020        0.6382             nan     0.0100   -0.0002
  1040        0.6361             nan     0.0100   -0.0002
  1060        0.6333             nan     0.0100   -0.0001
  1080        0.6313             nan     0.0100   -0.0001
  1100        0.6285             nan     0.0100   -0.0001

- Fold09.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2696             nan     0.1000    0.0285
     2        1.2226             nan     0.1000    0.0218
     3        1.1837             nan     0.1000    0.0181
     4        1.1503             nan     0.1000    0.0153
     5        1.1228             nan     0.1000    0.0119
     6        1.1015             nan     0.1000    0.0096
     7        1.0840             nan     0.1000    0.0069
     8        1.0656             nan     0.1000    0.0082
     9        1.0511             nan     0.1000    0.0069
    10        1.0367             nan     0.1000    0.0063
    20        0.9457             nan     0.1000    0.0019
    40        0.8640             nan     0.1000    0.0010
    60        0.8276             nan     0.1000   -0.0000
    80        0.8032             nan     0.1000   -0.0006
   100        0.7880             nan     0.1000   -0.0001
   120        0.7755             nan     0.1000   -0.0007
   140        0.7661             nan     0.1000   -0.0009
   160        0.7592             nan     0.1000   -0.0007
   180        0.7515             nan     0.1000   -0.0015
   200        0.7438             nan     0.1000   -0.0003
   220        0.7381             nan     0.1000   -0.0003
   240        0.7325             nan     0.1000   -0.0004
   260        0.7280             nan     0.1000   -0.0012
   280        0.7232             nan     0.1000   -0.0014
   300        0.7188             nan     0.1000   -0.0006
   320        0.7145             nan     0.1000   -0.0004
   340        0.7110             nan     0.1000   -0.0007
   360        0.7081             nan     0.1000   -0.0010
   380        0.7042             nan     0.1000   -0.0010
   400        0.6999             nan     0.1000   -0.0002
   420        0.6976             nan     0.1000   -0.0011
   440        0.6955             nan     0.1000   -0.0007
   460        0.6922             nan     0.1000   -0.0001
   480        0.6905             nan     0.1000   -0.0005
   500        0.6886             nan     0.1000   -0.0005
   520        0.6867             nan     0.1000   -0.0009
   540        0.6843             nan     0.1000   -0.0005
   560        0.6815             nan     0.1000   -0.0006
   580        0.6799             nan     0.1000   -0.0006
   600        0.6786             nan     0.1000   -0.0009
   620        0.6772             nan     0.1000   -0.0003
   640        0.6761             nan     0.1000   -0.0008
   660        0.6734             nan     0.1000   -0.0005
   680        0.6708             nan     0.1000   -0.0003
   700        0.6685             nan     0.1000   -0.0010
   720        0.6675             nan     0.1000   -0.0006
   740        0.6673             nan     0.1000   -0.0007
   760        0.6652             nan     0.1000   -0.0006
   780        0.6634             nan     0.1000   -0.0008
   800        0.6615             nan     0.1000   -0.0005
   820        0.6596             nan     0.1000   -0.0003
   840        0.6583             nan     0.1000   -0.0006
   860        0.6564             nan     0.1000   -0.0004
   880        0.6544             nan     0.1000   -0.0012
   900        0.6523             nan     0.1000   -0.0014
   920        0.6511             nan     0.1000   -0.0009
   940        0.6490             nan     0.1000   -0.0007
   960        0.6470             nan     0.1000   -0.0007
   980        0.6463             nan     0.1000   -0.0006
  1000        0.6445             nan     0.1000   -0.0011
  1020        0.6430             nan     0.1000   -0.0010
  1040        0.6406             nan     0.1000   -0.0011
  1060        0.6405             nan     0.1000   -0.0010
  1080        0.6395             nan     0.1000   -0.0010
  1100        0.6383             nan     0.1000   -0.0008

- Fold09.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2580             nan     0.1000    0.0372
     2        1.1998             nan     0.1000    0.0264
     3        1.1530             nan     0.1000    0.0232
     4        1.1130             nan     0.1000    0.0208
     5        1.0736             nan     0.1000    0.0178
     6        1.0433             nan     0.1000    0.0147
     7        1.0167             nan     0.1000    0.0100
     8        0.9983             nan     0.1000    0.0084
     9        0.9785             nan     0.1000    0.0091
    10        0.9645             nan     0.1000    0.0061
    20        0.8633             nan     0.1000    0.0023
    40        0.7859             nan     0.1000    0.0002
    60        0.7528             nan     0.1000   -0.0007
    80        0.7210             nan     0.1000   -0.0002
   100        0.6977             nan     0.1000   -0.0012
   120        0.6795             nan     0.1000   -0.0012
   140        0.6640             nan     0.1000   -0.0007
   160        0.6469             nan     0.1000   -0.0006
   180        0.6309             nan     0.1000   -0.0011
   200        0.6163             nan     0.1000   -0.0009
   220        0.6095             nan     0.1000   -0.0007
   240        0.5999             nan     0.1000   -0.0007
   260        0.5916             nan     0.1000   -0.0009
   280        0.5838             nan     0.1000   -0.0015
   300        0.5757             nan     0.1000   -0.0004
   320        0.5655             nan     0.1000   -0.0011
   340        0.5599             nan     0.1000   -0.0014
   360        0.5540             nan     0.1000   -0.0011
   380        0.5474             nan     0.1000   -0.0013
   400        0.5400             nan     0.1000   -0.0009
   420        0.5342             nan     0.1000   -0.0009
   440        0.5292             nan     0.1000   -0.0014
   460        0.5240             nan     0.1000   -0.0010
   480        0.5187             nan     0.1000   -0.0015
   500        0.5120             nan     0.1000   -0.0003
   520        0.5078             nan     0.1000   -0.0005
   540        0.5000             nan     0.1000   -0.0009
   560        0.4946             nan     0.1000   -0.0017
   580        0.4891             nan     0.1000   -0.0009
   600        0.4830             nan     0.1000   -0.0008
   620        0.4760             nan     0.1000   -0.0008
   640        0.4727             nan     0.1000   -0.0007
   660        0.4689             nan     0.1000   -0.0012
   680        0.4650             nan     0.1000   -0.0003
   700        0.4602             nan     0.1000   -0.0010
   720        0.4562             nan     0.1000   -0.0011
   740        0.4515             nan     0.1000   -0.0008
   760        0.4472             nan     0.1000   -0.0012
   780        0.4437             nan     0.1000   -0.0008
   800        0.4405             nan     0.1000   -0.0004
   820        0.4368             nan     0.1000   -0.0007
   840        0.4325             nan     0.1000   -0.0007
   860        0.4302             nan     0.1000   -0.0014
   880        0.4269             nan     0.1000   -0.0002
   900        0.4225             nan     0.1000   -0.0012
   920        0.4190             nan     0.1000   -0.0007
   940        0.4159             nan     0.1000   -0.0011
   960        0.4130             nan     0.1000   -0.0013
   980        0.4090             nan     0.1000   -0.0011
  1000        0.4042             nan     0.1000   -0.0007
  1020        0.4008             nan     0.1000   -0.0005
  1040        0.3974             nan     0.1000   -0.0005
  1060        0.3939             nan     0.1000   -0.0009
  1080        0.3918             nan     0.1000   -0.0012
  1100        0.3884             nan     0.1000   -0.0004

- Fold09.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2566             nan     0.1000    0.0366
     2        1.1935             nan     0.1000    0.0312
     3        1.1366             nan     0.1000    0.0255
     4        1.0906             nan     0.1000    0.0207
     5        1.0526             nan     0.1000    0.0177
     6        1.0216             nan     0.1000    0.0160
     7        0.9932             nan     0.1000    0.0114
     8        0.9667             nan     0.1000    0.0118
     9        0.9425             nan     0.1000    0.0113
    10        0.9236             nan     0.1000    0.0094
    20        0.8303             nan     0.1000    0.0011
    40        0.7456             nan     0.1000    0.0006
    60        0.6977             nan     0.1000   -0.0004
    80        0.6617             nan     0.1000   -0.0008
   100        0.6373             nan     0.1000   -0.0007
   120        0.6148             nan     0.1000   -0.0006
   140        0.5921             nan     0.1000   -0.0007
   160        0.5720             nan     0.1000   -0.0011
   180        0.5545             nan     0.1000   -0.0006
   200        0.5411             nan     0.1000   -0.0010
   220        0.5281             nan     0.1000   -0.0006
   240        0.5168             nan     0.1000   -0.0014
   260        0.5056             nan     0.1000   -0.0013
   280        0.4942             nan     0.1000   -0.0011
   300        0.4833             nan     0.1000   -0.0017
   320        0.4700             nan     0.1000   -0.0011
   340        0.4627             nan     0.1000   -0.0017
   360        0.4511             nan     0.1000   -0.0011
   380        0.4392             nan     0.1000   -0.0006
   400        0.4318             nan     0.1000   -0.0011
   420        0.4231             nan     0.1000   -0.0016
   440        0.4145             nan     0.1000   -0.0010
   460        0.4091             nan     0.1000   -0.0008
   480        0.4005             nan     0.1000   -0.0011
   500        0.3905             nan     0.1000   -0.0014
   520        0.3830             nan     0.1000   -0.0007
   540        0.3746             nan     0.1000   -0.0012
   560        0.3681             nan     0.1000   -0.0004
   580        0.3627             nan     0.1000   -0.0022
   600        0.3573             nan     0.1000   -0.0005
   620        0.3515             nan     0.1000   -0.0008
   640        0.3466             nan     0.1000   -0.0007
   660        0.3404             nan     0.1000   -0.0010
   680        0.3354             nan     0.1000   -0.0014
   700        0.3292             nan     0.1000   -0.0012
   720        0.3231             nan     0.1000   -0.0009
   740        0.3187             nan     0.1000   -0.0016
   760        0.3131             nan     0.1000   -0.0014
   780        0.3065             nan     0.1000   -0.0008
   800        0.3005             nan     0.1000   -0.0008
   820        0.2972             nan     0.1000   -0.0008
   840        0.2911             nan     0.1000   -0.0006
   860        0.2865             nan     0.1000   -0.0008
   880        0.2822             nan     0.1000   -0.0005
   900        0.2783             nan     0.1000   -0.0006
   920        0.2739             nan     0.1000   -0.0011
   940        0.2703             nan     0.1000   -0.0012
   960        0.2677             nan     0.1000   -0.0004
   980        0.2652             nan     0.1000   -0.0009
  1000        0.2626             nan     0.1000   -0.0014
  1020        0.2599             nan     0.1000   -0.0014
  1040        0.2571             nan     0.1000   -0.0012
  1060        0.2540             nan     0.1000   -0.0010
  1080        0.2516             nan     0.1000   -0.0004
  1100        0.2497             nan     0.1000   -0.0007

- Fold09.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3254             nan     0.0100    0.0031
     2        1.3190             nan     0.0100    0.0030
     3        1.3130             nan     0.0100    0.0032
     4        1.3071             nan     0.0100    0.0030
     5        1.3016             nan     0.0100    0.0028
     6        1.2954             nan     0.0100    0.0029
     7        1.2895             nan     0.0100    0.0027
     8        1.2838             nan     0.0100    0.0027
     9        1.2781             nan     0.0100    0.0026
    10        1.2730             nan     0.0100    0.0026
    20        1.2264             nan     0.0100    0.0021
    40        1.1554             nan     0.0100    0.0015
    60        1.1042             nan     0.0100    0.0011
    80        1.0670             nan     0.0100    0.0007
   100        1.0364             nan     0.0100    0.0006
   120        1.0118             nan     0.0100    0.0005
   140        0.9913             nan     0.0100    0.0003
   160        0.9740             nan     0.0100    0.0003
   180        0.9590             nan     0.0100    0.0003
   200        0.9463             nan     0.0100    0.0002
   220        0.9347             nan     0.0100    0.0002
   240        0.9248             nan     0.0100    0.0001
   260        0.9152             nan     0.0100    0.0001
   280        0.9070             nan     0.0100    0.0001
   300        0.8998             nan     0.0100    0.0001
   320        0.8929             nan     0.0100    0.0001
   340        0.8872             nan     0.0100    0.0000
   360        0.8812             nan     0.0100    0.0000
   380        0.8758             nan     0.0100    0.0000
   400        0.8713             nan     0.0100    0.0001
   420        0.8668             nan     0.0100    0.0000
   440        0.8623             nan     0.0100    0.0001
   460        0.8582             nan     0.0100    0.0000
   480        0.8546             nan     0.0100   -0.0001
   500        0.8508             nan     0.0100   -0.0000
   520        0.8472             nan     0.0100   -0.0000
   540        0.8437             nan     0.0100   -0.0000
   560        0.8406             nan     0.0100   -0.0000
   580        0.8375             nan     0.0100    0.0000
   600        0.8345             nan     0.0100    0.0000
   620        0.8318             nan     0.0100   -0.0000
   640        0.8295             nan     0.0100    0.0000
   660        0.8268             nan     0.0100    0.0000
   680        0.8243             nan     0.0100   -0.0001
   700        0.8220             nan     0.0100    0.0000
   720        0.8196             nan     0.0100   -0.0000
   740        0.8173             nan     0.0100   -0.0001
   760        0.8151             nan     0.0100   -0.0000
   780        0.8130             nan     0.0100    0.0000
   800        0.8108             nan     0.0100   -0.0001
   820        0.8090             nan     0.0100   -0.0000
   840        0.8070             nan     0.0100   -0.0000
   860        0.8054             nan     0.0100   -0.0000
   880        0.8036             nan     0.0100   -0.0000
   900        0.8020             nan     0.0100   -0.0000
   920        0.8001             nan     0.0100   -0.0001
   940        0.7983             nan     0.0100   -0.0000
   960        0.7968             nan     0.0100   -0.0001
   980        0.7954             nan     0.0100   -0.0000
  1000        0.7937             nan     0.0100   -0.0000
  1020        0.7922             nan     0.0100   -0.0000
  1040        0.7907             nan     0.0100   -0.0001
  1060        0.7894             nan     0.0100   -0.0001
  1080        0.7880             nan     0.0100   -0.0000
  1100        0.7867             nan     0.0100   -0.0000

- Fold10.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0038
     2        1.3169             nan     0.0100    0.0037
     3        1.3092             nan     0.0100    0.0036
     4        1.3023             nan     0.0100    0.0033
     5        1.2955             nan     0.0100    0.0034
     6        1.2885             nan     0.0100    0.0034
     7        1.2817             nan     0.0100    0.0036
     8        1.2752             nan     0.0100    0.0032
     9        1.2683             nan     0.0100    0.0030
    10        1.2621             nan     0.0100    0.0032
    20        1.2041             nan     0.0100    0.0025
    40        1.1145             nan     0.0100    0.0017
    60        1.0504             nan     0.0100    0.0011
    80        1.0007             nan     0.0100    0.0010
   100        0.9622             nan     0.0100    0.0007
   120        0.9329             nan     0.0100    0.0003
   140        0.9104             nan     0.0100    0.0003
   160        0.8925             nan     0.0100    0.0002
   180        0.8785             nan     0.0100    0.0000
   200        0.8660             nan     0.0100    0.0000
   220        0.8551             nan     0.0100    0.0001
   240        0.8449             nan     0.0100    0.0001
   260        0.8362             nan     0.0100    0.0001
   280        0.8286             nan     0.0100    0.0000
   300        0.8204             nan     0.0100    0.0001
   320        0.8138             nan     0.0100    0.0000
   340        0.8076             nan     0.0100   -0.0000
   360        0.8018             nan     0.0100    0.0001
   380        0.7972             nan     0.0100    0.0000
   400        0.7926             nan     0.0100   -0.0000
   420        0.7882             nan     0.0100   -0.0000
   440        0.7840             nan     0.0100   -0.0001
   460        0.7798             nan     0.0100    0.0000
   480        0.7758             nan     0.0100    0.0000
   500        0.7718             nan     0.0100   -0.0000
   520        0.7674             nan     0.0100    0.0000
   540        0.7639             nan     0.0100   -0.0001
   560        0.7607             nan     0.0100   -0.0001
   580        0.7575             nan     0.0100    0.0000
   600        0.7546             nan     0.0100   -0.0000
   620        0.7515             nan     0.0100   -0.0001
   640        0.7488             nan     0.0100   -0.0001
   660        0.7460             nan     0.0100   -0.0001
   680        0.7431             nan     0.0100   -0.0000
   700        0.7401             nan     0.0100   -0.0001
   720        0.7378             nan     0.0100   -0.0001
   740        0.7350             nan     0.0100   -0.0000
   760        0.7322             nan     0.0100   -0.0002
   780        0.7295             nan     0.0100   -0.0001
   800        0.7269             nan     0.0100   -0.0000
   820        0.7247             nan     0.0100   -0.0001
   840        0.7224             nan     0.0100   -0.0001
   860        0.7202             nan     0.0100   -0.0000
   880        0.7181             nan     0.0100   -0.0001
   900        0.7160             nan     0.0100   -0.0001
   920        0.7134             nan     0.0100   -0.0001
   940        0.7110             nan     0.0100   -0.0001
   960        0.7092             nan     0.0100   -0.0002
   980        0.7066             nan     0.0100   -0.0001
  1000        0.7047             nan     0.0100   -0.0001
  1020        0.7027             nan     0.0100   -0.0000
  1040        0.7010             nan     0.0100   -0.0001
  1060        0.6990             nan     0.0100   -0.0000
  1080        0.6972             nan     0.0100   -0.0000
  1100        0.6952             nan     0.0100   -0.0001

- Fold10.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3233             nan     0.0100    0.0041
     2        1.3148             nan     0.0100    0.0038
     3        1.3070             nan     0.0100    0.0036
     4        1.2989             nan     0.0100    0.0037
     5        1.2912             nan     0.0100    0.0040
     6        1.2835             nan     0.0100    0.0038
     7        1.2760             nan     0.0100    0.0035
     8        1.2685             nan     0.0100    0.0035
     9        1.2615             nan     0.0100    0.0034
    10        1.2547             nan     0.0100    0.0032
    20        1.1915             nan     0.0100    0.0030
    40        1.0950             nan     0.0100    0.0020
    60        1.0245             nan     0.0100    0.0015
    80        0.9721             nan     0.0100    0.0011
   100        0.9328             nan     0.0100    0.0009
   120        0.9032             nan     0.0100    0.0006
   140        0.8790             nan     0.0100    0.0004
   160        0.8594             nan     0.0100    0.0001
   180        0.8426             nan     0.0100    0.0003
   200        0.8287             nan     0.0100    0.0001
   220        0.8168             nan     0.0100    0.0001
   240        0.8075             nan     0.0100    0.0001
   260        0.7980             nan     0.0100    0.0001
   280        0.7894             nan     0.0100    0.0001
   300        0.7813             nan     0.0100    0.0000
   320        0.7741             nan     0.0100    0.0001
   340        0.7669             nan     0.0100   -0.0000
   360        0.7605             nan     0.0100   -0.0000
   380        0.7544             nan     0.0100    0.0001
   400        0.7494             nan     0.0100   -0.0002
   420        0.7444             nan     0.0100   -0.0001
   440        0.7396             nan     0.0100   -0.0000
   460        0.7349             nan     0.0100   -0.0001
   480        0.7300             nan     0.0100   -0.0001
   500        0.7252             nan     0.0100   -0.0001
   520        0.7208             nan     0.0100   -0.0001
   540        0.7171             nan     0.0100   -0.0001
   560        0.7134             nan     0.0100   -0.0002
   580        0.7094             nan     0.0100   -0.0001
   600        0.7057             nan     0.0100   -0.0001
   620        0.7021             nan     0.0100   -0.0001
   640        0.6985             nan     0.0100   -0.0001
   660        0.6959             nan     0.0100   -0.0001
   680        0.6928             nan     0.0100   -0.0001
   700        0.6894             nan     0.0100   -0.0002
   720        0.6863             nan     0.0100   -0.0001
   740        0.6829             nan     0.0100   -0.0002
   760        0.6799             nan     0.0100   -0.0001
   780        0.6774             nan     0.0100   -0.0000
   800        0.6744             nan     0.0100   -0.0001
   820        0.6713             nan     0.0100   -0.0002
   840        0.6687             nan     0.0100   -0.0001
   860        0.6659             nan     0.0100   -0.0001
   880        0.6633             nan     0.0100   -0.0002
   900        0.6603             nan     0.0100   -0.0001
   920        0.6576             nan     0.0100   -0.0000
   940        0.6547             nan     0.0100   -0.0001
   960        0.6518             nan     0.0100   -0.0001
   980        0.6495             nan     0.0100   -0.0001
  1000        0.6477             nan     0.0100   -0.0001
  1020        0.6453             nan     0.0100   -0.0001
  1040        0.6430             nan     0.0100   -0.0002
  1060        0.6406             nan     0.0100   -0.0001
  1080        0.6383             nan     0.0100   -0.0001
  1100        0.6357             nan     0.0100   -0.0001

- Fold10.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2763             nan     0.1000    0.0297
     2        1.2289             nan     0.1000    0.0251
     3        1.1877             nan     0.1000    0.0203
     4        1.1511             nan     0.1000    0.0163
     5        1.1208             nan     0.1000    0.0133
     6        1.0997             nan     0.1000    0.0111
     7        1.0819             nan     0.1000    0.0066
     8        1.0674             nan     0.1000    0.0065
     9        1.0477             nan     0.1000    0.0085
    10        1.0331             nan     0.1000    0.0072
    20        0.9397             nan     0.1000    0.0020
    40        0.8680             nan     0.1000    0.0010
    60        0.8332             nan     0.1000   -0.0007
    80        0.8123             nan     0.1000   -0.0009
   100        0.7940             nan     0.1000    0.0001
   120        0.7795             nan     0.1000   -0.0007
   140        0.7670             nan     0.1000   -0.0007
   160        0.7586             nan     0.1000   -0.0011
   180        0.7516             nan     0.1000   -0.0010
   200        0.7438             nan     0.1000   -0.0000
   220        0.7383             nan     0.1000   -0.0005
   240        0.7339             nan     0.1000   -0.0009
   260        0.7295             nan     0.1000   -0.0011
   280        0.7252             nan     0.1000   -0.0007
   300        0.7213             nan     0.1000   -0.0016
   320        0.7173             nan     0.1000   -0.0007
   340        0.7127             nan     0.1000   -0.0007
   360        0.7095             nan     0.1000   -0.0006
   380        0.7067             nan     0.1000   -0.0006
   400        0.7036             nan     0.1000   -0.0006
   420        0.7000             nan     0.1000   -0.0007
   440        0.6974             nan     0.1000   -0.0007
   460        0.6930             nan     0.1000   -0.0009
   480        0.6916             nan     0.1000   -0.0006
   500        0.6889             nan     0.1000   -0.0010
   520        0.6852             nan     0.1000   -0.0003
   540        0.6825             nan     0.1000   -0.0007
   560        0.6812             nan     0.1000   -0.0015
   580        0.6782             nan     0.1000   -0.0007
   600        0.6749             nan     0.1000   -0.0011
   620        0.6731             nan     0.1000   -0.0003
   640        0.6698             nan     0.1000   -0.0013
   660        0.6679             nan     0.1000   -0.0005
   680        0.6668             nan     0.1000   -0.0012
   700        0.6660             nan     0.1000   -0.0008
   720        0.6634             nan     0.1000   -0.0005
   740        0.6613             nan     0.1000   -0.0008
   760        0.6597             nan     0.1000   -0.0012
   780        0.6582             nan     0.1000   -0.0002
   800        0.6561             nan     0.1000   -0.0018
   820        0.6548             nan     0.1000   -0.0007
   840        0.6533             nan     0.1000   -0.0005
   860        0.6510             nan     0.1000   -0.0005
   880        0.6494             nan     0.1000   -0.0004
   900        0.6483             nan     0.1000   -0.0007
   920        0.6486             nan     0.1000   -0.0012
   940        0.6461             nan     0.1000   -0.0006
   960        0.6441             nan     0.1000   -0.0012
   980        0.6418             nan     0.1000   -0.0008
  1000        0.6395             nan     0.1000   -0.0003
  1020        0.6367             nan     0.1000   -0.0005
  1040        0.6352             nan     0.1000   -0.0002
  1060        0.6344             nan     0.1000   -0.0004
  1080        0.6332             nan     0.1000   -0.0013
  1100        0.6316             nan     0.1000   -0.0013

- Fold10.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2651             nan     0.1000    0.0386
     2        1.2053             nan     0.1000    0.0307
     3        1.1551             nan     0.1000    0.0250
     4        1.1105             nan     0.1000    0.0197
     5        1.0745             nan     0.1000    0.0173
     6        1.0458             nan     0.1000    0.0130
     7        1.0203             nan     0.1000    0.0114
     8        0.9973             nan     0.1000    0.0116
     9        0.9773             nan     0.1000    0.0080
    10        0.9620             nan     0.1000    0.0072
    20        0.8612             nan     0.1000    0.0007
    40        0.7926             nan     0.1000   -0.0000
    60        0.7575             nan     0.1000   -0.0002
    80        0.7311             nan     0.1000   -0.0004
   100        0.7087             nan     0.1000    0.0003
   120        0.6902             nan     0.1000   -0.0015
   140        0.6767             nan     0.1000   -0.0014
   160        0.6605             nan     0.1000   -0.0009
   180        0.6460             nan     0.1000   -0.0008
   200        0.6338             nan     0.1000   -0.0004
   220        0.6231             nan     0.1000   -0.0014
   240        0.6121             nan     0.1000   -0.0008
   260        0.6036             nan     0.1000   -0.0004
   280        0.5923             nan     0.1000   -0.0015
   300        0.5798             nan     0.1000   -0.0007
   320        0.5717             nan     0.1000   -0.0009
   340        0.5636             nan     0.1000   -0.0008
   360        0.5546             nan     0.1000   -0.0006
   380        0.5499             nan     0.1000   -0.0010
   400        0.5438             nan     0.1000   -0.0009
   420        0.5383             nan     0.1000   -0.0017
   440        0.5314             nan     0.1000   -0.0009
   460        0.5233             nan     0.1000   -0.0010
   480        0.5156             nan     0.1000   -0.0009
   500        0.5092             nan     0.1000   -0.0013
   520        0.5014             nan     0.1000   -0.0014
   540        0.4963             nan     0.1000   -0.0010
   560        0.4904             nan     0.1000   -0.0010
   580        0.4849             nan     0.1000   -0.0008
   600        0.4790             nan     0.1000   -0.0012
   620        0.4755             nan     0.1000   -0.0008
   640        0.4702             nan     0.1000   -0.0012
   660        0.4651             nan     0.1000   -0.0010
   680        0.4580             nan     0.1000   -0.0010
   700        0.4534             nan     0.1000   -0.0006
   720        0.4488             nan     0.1000   -0.0007
   740        0.4452             nan     0.1000   -0.0006
   760        0.4415             nan     0.1000   -0.0006
   780        0.4382             nan     0.1000   -0.0015
   800        0.4334             nan     0.1000   -0.0008
   820        0.4293             nan     0.1000   -0.0005
   840        0.4263             nan     0.1000   -0.0008
   860        0.4230             nan     0.1000   -0.0016
   880        0.4208             nan     0.1000   -0.0006
   900        0.4170             nan     0.1000   -0.0009
   920        0.4122             nan     0.1000   -0.0008
   940        0.4089             nan     0.1000   -0.0008
   960        0.4059             nan     0.1000   -0.0005
   980        0.4023             nan     0.1000   -0.0007
  1000        0.3974             nan     0.1000   -0.0007
  1020        0.3931             nan     0.1000   -0.0006
  1040        0.3895             nan     0.1000   -0.0007
  1060        0.3864             nan     0.1000   -0.0005
  1080        0.3838             nan     0.1000   -0.0012
  1100        0.3814             nan     0.1000   -0.0009

- Fold10.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2520             nan     0.1000    0.0367
     2        1.1895             nan     0.1000    0.0327
     3        1.1401             nan     0.1000    0.0237
     4        1.0953             nan     0.1000    0.0204
     5        1.0594             nan     0.1000    0.0155
     6        1.0278             nan     0.1000    0.0155
     7        0.9948             nan     0.1000    0.0149
     8        0.9699             nan     0.1000    0.0126
     9        0.9472             nan     0.1000    0.0100
    10        0.9285             nan     0.1000    0.0073
    20        0.8250             nan     0.1000    0.0016
    40        0.7467             nan     0.1000   -0.0003
    60        0.7058             nan     0.1000   -0.0009
    80        0.6704             nan     0.1000   -0.0012
   100        0.6415             nan     0.1000   -0.0013
   120        0.6197             nan     0.1000   -0.0014
   140        0.6027             nan     0.1000   -0.0012
   160        0.5863             nan     0.1000   -0.0008
   180        0.5699             nan     0.1000   -0.0010
   200        0.5530             nan     0.1000   -0.0012
   220        0.5371             nan     0.1000   -0.0008
   240        0.5199             nan     0.1000   -0.0002
   260        0.5070             nan     0.1000   -0.0018
   280        0.4974             nan     0.1000   -0.0012
   300        0.4835             nan     0.1000   -0.0008
   320        0.4740             nan     0.1000   -0.0007
   340        0.4661             nan     0.1000   -0.0011
   360        0.4557             nan     0.1000   -0.0011
   380        0.4476             nan     0.1000   -0.0007
   400        0.4371             nan     0.1000   -0.0005
   420        0.4314             nan     0.1000   -0.0006
   440        0.4213             nan     0.1000   -0.0019
   460        0.4134             nan     0.1000   -0.0009
   480        0.4051             nan     0.1000   -0.0005
   500        0.4001             nan     0.1000   -0.0007
   520        0.3941             nan     0.1000   -0.0009
   540        0.3879             nan     0.1000   -0.0006
   560        0.3809             nan     0.1000   -0.0015
   580        0.3725             nan     0.1000   -0.0006
   600        0.3685             nan     0.1000   -0.0010
   620        0.3608             nan     0.1000   -0.0016
   640        0.3533             nan     0.1000   -0.0007
   660        0.3480             nan     0.1000   -0.0006
   680        0.3428             nan     0.1000   -0.0009
   700        0.3375             nan     0.1000   -0.0005
   720        0.3336             nan     0.1000   -0.0006
   740        0.3288             nan     0.1000   -0.0009
   760        0.3250             nan     0.1000   -0.0004
   780        0.3203             nan     0.1000   -0.0011
   800        0.3145             nan     0.1000   -0.0006
   820        0.3106             nan     0.1000   -0.0004
   840        0.3070             nan     0.1000   -0.0012
   860        0.3029             nan     0.1000   -0.0008
   880        0.2995             nan     0.1000   -0.0006
   900        0.2962             nan     0.1000   -0.0012
   920        0.2921             nan     0.1000   -0.0009
   940        0.2874             nan     0.1000   -0.0011
   960        0.2830             nan     0.1000   -0.0007
   980        0.2789             nan     0.1000   -0.0006
  1000        0.2757             nan     0.1000   -0.0002
  1020        0.2732             nan     0.1000   -0.0015
  1040        0.2687             nan     0.1000   -0.0004
  1060        0.2660             nan     0.1000   -0.0007
  1080        0.2630             nan     0.1000   -0.0007
  1100        0.2592             nan     0.1000   -0.0006

- Fold10.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0029
     2        1.3203             nan     0.0100    0.0029
     3        1.3142             nan     0.0100    0.0029
     4        1.3081             nan     0.0100    0.0028
     5        1.3022             nan     0.0100    0.0027
     6        1.2966             nan     0.0100    0.0026
     7        1.2912             nan     0.0100    0.0026
     8        1.2858             nan     0.0100    0.0026
     9        1.2808             nan     0.0100    0.0026
    10        1.2756             nan     0.0100    0.0025
    20        1.2293             nan     0.0100    0.0020
    40        1.1601             nan     0.0100    0.0014
    60        1.1106             nan     0.0100    0.0010
    80        1.0728             nan     0.0100    0.0008
   100        1.0425             nan     0.0100    0.0007
   120        1.0183             nan     0.0100    0.0003
   140        0.9969             nan     0.0100    0.0004
   160        0.9786             nan     0.0100    0.0004
   180        0.9629             nan     0.0100    0.0002
   200        0.9496             nan     0.0100    0.0002
   220        0.9376             nan     0.0100    0.0002
   240        0.9270             nan     0.0100    0.0002
   260        0.9173             nan     0.0100    0.0001
   280        0.9094             nan     0.0100    0.0001
   300        0.9012             nan     0.0100    0.0001
   320        0.8941             nan     0.0100    0.0001
   340        0.8867             nan     0.0100    0.0001
   360        0.8807             nan     0.0100    0.0002
   380        0.8751             nan     0.0100    0.0000
   400        0.8697             nan     0.0100    0.0001
   420        0.8646             nan     0.0100    0.0000
   440        0.8597             nan     0.0100    0.0000
   460        0.8555             nan     0.0100    0.0001
   480        0.8512             nan     0.0100    0.0000
   500        0.8474             nan     0.0100    0.0001
   520        0.8436             nan     0.0100    0.0000
   540        0.8399             nan     0.0100    0.0000
   560        0.8363             nan     0.0100    0.0000
   580        0.8328             nan     0.0100    0.0000
   600        0.8297             nan     0.0100    0.0000
   620        0.8265             nan     0.0100    0.0000
   640        0.8236             nan     0.0100    0.0000
   660        0.8211             nan     0.0100   -0.0001
   680        0.8185             nan     0.0100   -0.0000
   700        0.8157             nan     0.0100   -0.0000
   720        0.8131             nan     0.0100   -0.0000
   740        0.8108             nan     0.0100   -0.0000
   760        0.8086             nan     0.0100   -0.0000
   780        0.8067             nan     0.0100   -0.0000
   800        0.8042             nan     0.0100   -0.0001
   820        0.8020             nan     0.0100    0.0000
   840        0.7998             nan     0.0100   -0.0000
   860        0.7977             nan     0.0100   -0.0000
   880        0.7957             nan     0.0100   -0.0000
   900        0.7938             nan     0.0100   -0.0001
   920        0.7919             nan     0.0100   -0.0000
   940        0.7902             nan     0.0100   -0.0000
   960        0.7885             nan     0.0100   -0.0000
   980        0.7870             nan     0.0100   -0.0001
  1000        0.7853             nan     0.0100   -0.0000
  1020        0.7836             nan     0.0100   -0.0000
  1040        0.7822             nan     0.0100   -0.0000
  1060        0.7806             nan     0.0100   -0.0001
  1080        0.7790             nan     0.0100   -0.0000
  1100        0.7775             nan     0.0100   -0.0001

- Fold01.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0036
     2        1.3163             nan     0.0100    0.0036
     3        1.3092             nan     0.0100    0.0035
     4        1.3018             nan     0.0100    0.0037
     5        1.2949             nan     0.0100    0.0035
     6        1.2878             nan     0.0100    0.0035
     7        1.2814             nan     0.0100    0.0035
     8        1.2752             nan     0.0100    0.0029
     9        1.2684             nan     0.0100    0.0033
    10        1.2619             nan     0.0100    0.0031
    20        1.2040             nan     0.0100    0.0026
    40        1.1149             nan     0.0100    0.0019
    60        1.0496             nan     0.0100    0.0013
    80        1.0006             nan     0.0100    0.0010
   100        0.9630             nan     0.0100    0.0008
   120        0.9331             nan     0.0100    0.0006
   140        0.9090             nan     0.0100    0.0004
   160        0.8910             nan     0.0100    0.0002
   180        0.8742             nan     0.0100    0.0002
   200        0.8603             nan     0.0100    0.0002
   220        0.8486             nan     0.0100    0.0001
   240        0.8384             nan     0.0100    0.0002
   260        0.8296             nan     0.0100    0.0000
   280        0.8216             nan     0.0100    0.0000
   300        0.8138             nan     0.0100    0.0001
   320        0.8066             nan     0.0100    0.0001
   340        0.7999             nan     0.0100    0.0000
   360        0.7933             nan     0.0100    0.0000
   380        0.7870             nan     0.0100    0.0002
   400        0.7815             nan     0.0100    0.0000
   420        0.7765             nan     0.0100   -0.0001
   440        0.7717             nan     0.0100    0.0000
   460        0.7669             nan     0.0100    0.0001
   480        0.7628             nan     0.0100   -0.0001
   500        0.7589             nan     0.0100   -0.0001
   520        0.7555             nan     0.0100    0.0001
   540        0.7518             nan     0.0100   -0.0000
   560        0.7485             nan     0.0100   -0.0000
   580        0.7454             nan     0.0100    0.0001
   600        0.7421             nan     0.0100   -0.0000
   620        0.7387             nan     0.0100   -0.0000
   640        0.7353             nan     0.0100    0.0000
   660        0.7323             nan     0.0100   -0.0001
   680        0.7298             nan     0.0100   -0.0001
   700        0.7270             nan     0.0100   -0.0000
   720        0.7244             nan     0.0100   -0.0000
   740        0.7219             nan     0.0100   -0.0001
   760        0.7197             nan     0.0100    0.0000
   780        0.7173             nan     0.0100   -0.0001
   800        0.7151             nan     0.0100   -0.0000
   820        0.7127             nan     0.0100   -0.0000
   840        0.7106             nan     0.0100   -0.0002
   860        0.7083             nan     0.0100   -0.0001
   880        0.7059             nan     0.0100   -0.0001
   900        0.7039             nan     0.0100   -0.0002
   920        0.7020             nan     0.0100   -0.0001
   940        0.6997             nan     0.0100   -0.0001
   960        0.6977             nan     0.0100   -0.0000
   980        0.6956             nan     0.0100   -0.0001
  1000        0.6938             nan     0.0100   -0.0000
  1020        0.6920             nan     0.0100   -0.0000
  1040        0.6901             nan     0.0100   -0.0001
  1060        0.6884             nan     0.0100   -0.0000
  1080        0.6864             nan     0.0100   -0.0001
  1100        0.6847             nan     0.0100   -0.0002

- Fold01.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3238             nan     0.0100    0.0038
     2        1.3157             nan     0.0100    0.0037
     3        1.3074             nan     0.0100    0.0040
     4        1.3000             nan     0.0100    0.0036
     5        1.2925             nan     0.0100    0.0039
     6        1.2848             nan     0.0100    0.0035
     7        1.2773             nan     0.0100    0.0037
     8        1.2702             nan     0.0100    0.0035
     9        1.2625             nan     0.0100    0.0035
    10        1.2554             nan     0.0100    0.0035
    20        1.1921             nan     0.0100    0.0028
    40        1.0920             nan     0.0100    0.0020
    60        1.0205             nan     0.0100    0.0015
    80        0.9677             nan     0.0100    0.0007
   100        0.9264             nan     0.0100    0.0009
   120        0.8960             nan     0.0100    0.0006
   140        0.8710             nan     0.0100    0.0005
   160        0.8508             nan     0.0100    0.0005
   180        0.8348             nan     0.0100    0.0002
   200        0.8208             nan     0.0100    0.0003
   220        0.8085             nan     0.0100    0.0001
   240        0.7984             nan     0.0100    0.0002
   260        0.7875             nan     0.0100    0.0001
   280        0.7776             nan     0.0100    0.0000
   300        0.7695             nan     0.0100    0.0001
   320        0.7620             nan     0.0100    0.0001
   340        0.7547             nan     0.0100   -0.0000
   360        0.7484             nan     0.0100   -0.0002
   380        0.7423             nan     0.0100   -0.0001
   400        0.7369             nan     0.0100   -0.0000
   420        0.7312             nan     0.0100   -0.0001
   440        0.7257             nan     0.0100   -0.0001
   460        0.7208             nan     0.0100    0.0000
   480        0.7165             nan     0.0100   -0.0002
   500        0.7118             nan     0.0100   -0.0000
   520        0.7072             nan     0.0100   -0.0000
   540        0.7029             nan     0.0100   -0.0001
   560        0.6991             nan     0.0100   -0.0000
   580        0.6953             nan     0.0100   -0.0001
   600        0.6915             nan     0.0100   -0.0002
   620        0.6880             nan     0.0100    0.0000
   640        0.6844             nan     0.0100   -0.0001
   660        0.6811             nan     0.0100    0.0001
   680        0.6781             nan     0.0100   -0.0000
   700        0.6753             nan     0.0100   -0.0001
   720        0.6721             nan     0.0100   -0.0001
   740        0.6692             nan     0.0100   -0.0000
   760        0.6659             nan     0.0100   -0.0002
   780        0.6622             nan     0.0100   -0.0000
   800        0.6595             nan     0.0100   -0.0001
   820        0.6569             nan     0.0100   -0.0000
   840        0.6540             nan     0.0100   -0.0001
   860        0.6510             nan     0.0100   -0.0001
   880        0.6482             nan     0.0100   -0.0002
   900        0.6460             nan     0.0100    0.0000
   920        0.6431             nan     0.0100    0.0000
   940        0.6403             nan     0.0100   -0.0001
   960        0.6376             nan     0.0100   -0.0001
   980        0.6350             nan     0.0100   -0.0001
  1000        0.6323             nan     0.0100   -0.0001
  1020        0.6295             nan     0.0100   -0.0001
  1040        0.6267             nan     0.0100   -0.0001
  1060        0.6246             nan     0.0100   -0.0001
  1080        0.6222             nan     0.0100   -0.0001
  1100        0.6198             nan     0.0100   -0.0001

- Fold01.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2709             nan     0.1000    0.0277
     2        1.2237             nan     0.1000    0.0234
     3        1.1882             nan     0.1000    0.0191
     4        1.1583             nan     0.1000    0.0161
     5        1.1354             nan     0.1000    0.0129
     6        1.1094             nan     0.1000    0.0112
     7        1.0900             nan     0.1000    0.0087
     8        1.0754             nan     0.1000    0.0066
     9        1.0557             nan     0.1000    0.0085
    10        1.0410             nan     0.1000    0.0071
    20        0.9473             nan     0.1000    0.0028
    40        0.8702             nan     0.1000    0.0004
    60        0.8291             nan     0.1000   -0.0004
    80        0.8038             nan     0.1000    0.0001
   100        0.7848             nan     0.1000   -0.0015
   120        0.7717             nan     0.1000   -0.0005
   140        0.7597             nan     0.1000   -0.0000
   160        0.7524             nan     0.1000   -0.0006
   180        0.7443             nan     0.1000   -0.0008
   200        0.7374             nan     0.1000   -0.0007
   220        0.7309             nan     0.1000   -0.0012
   240        0.7269             nan     0.1000   -0.0010
   260        0.7224             nan     0.1000   -0.0012
   280        0.7180             nan     0.1000   -0.0006
   300        0.7145             nan     0.1000   -0.0010
   320        0.7095             nan     0.1000   -0.0011
   340        0.7059             nan     0.1000   -0.0009
   360        0.7007             nan     0.1000   -0.0004
   380        0.6978             nan     0.1000   -0.0004
   400        0.6939             nan     0.1000   -0.0011
   420        0.6898             nan     0.1000   -0.0008
   440        0.6868             nan     0.1000   -0.0009
   460        0.6844             nan     0.1000   -0.0016
   480        0.6811             nan     0.1000   -0.0005
   500        0.6790             nan     0.1000   -0.0005
   520        0.6762             nan     0.1000   -0.0007
   540        0.6741             nan     0.1000   -0.0009
   560        0.6718             nan     0.1000   -0.0005
   580        0.6694             nan     0.1000   -0.0005
   600        0.6681             nan     0.1000   -0.0012
   620        0.6656             nan     0.1000   -0.0009
   640        0.6637             nan     0.1000   -0.0005
   660        0.6604             nan     0.1000   -0.0009
   680        0.6589             nan     0.1000   -0.0003
   700        0.6570             nan     0.1000   -0.0003
   720        0.6560             nan     0.1000   -0.0007
   740        0.6547             nan     0.1000   -0.0011
   760        0.6531             nan     0.1000   -0.0015
   780        0.6512             nan     0.1000   -0.0008
   800        0.6494             nan     0.1000   -0.0005
   820        0.6462             nan     0.1000   -0.0007
   840        0.6442             nan     0.1000   -0.0002
   860        0.6428             nan     0.1000   -0.0008
   880        0.6415             nan     0.1000   -0.0007
   900        0.6405             nan     0.1000   -0.0008
   920        0.6393             nan     0.1000   -0.0008
   940        0.6382             nan     0.1000   -0.0008
   960        0.6361             nan     0.1000   -0.0002
   980        0.6347             nan     0.1000   -0.0013
  1000        0.6329             nan     0.1000   -0.0014
  1020        0.6303             nan     0.1000   -0.0008
  1040        0.6285             nan     0.1000   -0.0009
  1060        0.6275             nan     0.1000   -0.0002
  1080        0.6254             nan     0.1000   -0.0008
  1100        0.6240             nan     0.1000   -0.0012

- Fold01.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2591             nan     0.1000    0.0361
     2        1.1981             nan     0.1000    0.0280
     3        1.1441             nan     0.1000    0.0240
     4        1.1081             nan     0.1000    0.0209
     5        1.0707             nan     0.1000    0.0167
     6        1.0402             nan     0.1000    0.0136
     7        1.0143             nan     0.1000    0.0119
     8        0.9922             nan     0.1000    0.0105
     9        0.9712             nan     0.1000    0.0099
    10        0.9543             nan     0.1000    0.0086
    20        0.8583             nan     0.1000    0.0013
    40        0.7817             nan     0.1000    0.0015
    60        0.7428             nan     0.1000    0.0002
    80        0.7153             nan     0.1000   -0.0013
   100        0.6906             nan     0.1000   -0.0010
   120        0.6723             nan     0.1000   -0.0006
   140        0.6542             nan     0.1000   -0.0014
   160        0.6416             nan     0.1000   -0.0009
   180        0.6298             nan     0.1000   -0.0006
   200        0.6171             nan     0.1000   -0.0006
   220        0.6052             nan     0.1000   -0.0009
   240        0.5982             nan     0.1000   -0.0012
   260        0.5899             nan     0.1000   -0.0007
   280        0.5796             nan     0.1000   -0.0011
   300        0.5705             nan     0.1000   -0.0015
   320        0.5617             nan     0.1000   -0.0006
   340        0.5555             nan     0.1000   -0.0011
   360        0.5493             nan     0.1000   -0.0005
   380        0.5423             nan     0.1000   -0.0014
   400        0.5349             nan     0.1000   -0.0004
   420        0.5305             nan     0.1000   -0.0009
   440        0.5238             nan     0.1000   -0.0012
   460        0.5182             nan     0.1000   -0.0004
   480        0.5134             nan     0.1000   -0.0015
   500        0.5058             nan     0.1000   -0.0006
   520        0.4990             nan     0.1000   -0.0010
   540        0.4928             nan     0.1000   -0.0011
   560        0.4867             nan     0.1000   -0.0007
   580        0.4801             nan     0.1000   -0.0010
   600        0.4749             nan     0.1000   -0.0003
   620        0.4708             nan     0.1000   -0.0003
   640        0.4667             nan     0.1000   -0.0011
   660        0.4625             nan     0.1000   -0.0011
   680        0.4588             nan     0.1000   -0.0018
   700        0.4550             nan     0.1000   -0.0010
   720        0.4508             nan     0.1000   -0.0004
   740        0.4463             nan     0.1000   -0.0007
   760        0.4423             nan     0.1000   -0.0008
   780        0.4381             nan     0.1000   -0.0009
   800        0.4323             nan     0.1000   -0.0006
   820        0.4287             nan     0.1000   -0.0010
   840        0.4264             nan     0.1000   -0.0009
   860        0.4224             nan     0.1000   -0.0010
   880        0.4178             nan     0.1000   -0.0012
   900        0.4149             nan     0.1000   -0.0012
   920        0.4099             nan     0.1000   -0.0009
   940        0.4070             nan     0.1000   -0.0009
   960        0.4038             nan     0.1000   -0.0008
   980        0.4021             nan     0.1000   -0.0006
  1000        0.4001             nan     0.1000   -0.0009
  1020        0.3966             nan     0.1000   -0.0010
  1040        0.3928             nan     0.1000   -0.0012
  1060        0.3893             nan     0.1000   -0.0008
  1080        0.3868             nan     0.1000   -0.0009
  1100        0.3837             nan     0.1000   -0.0008

- Fold01.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2556             nan     0.1000    0.0393
     2        1.1935             nan     0.1000    0.0310
     3        1.1336             nan     0.1000    0.0268
     4        1.0860             nan     0.1000    0.0203
     5        1.0488             nan     0.1000    0.0171
     6        1.0163             nan     0.1000    0.0165
     7        0.9888             nan     0.1000    0.0097
     8        0.9635             nan     0.1000    0.0117
     9        0.9404             nan     0.1000    0.0088
    10        0.9221             nan     0.1000    0.0073
    20        0.8173             nan     0.1000   -0.0001
    40        0.7421             nan     0.1000   -0.0003
    60        0.6947             nan     0.1000   -0.0005
    80        0.6601             nan     0.1000   -0.0000
   100        0.6339             nan     0.1000   -0.0006
   120        0.6112             nan     0.1000   -0.0011
   140        0.5914             nan     0.1000   -0.0028
   160        0.5776             nan     0.1000   -0.0014
   180        0.5581             nan     0.1000   -0.0011
   200        0.5446             nan     0.1000   -0.0014
   220        0.5290             nan     0.1000   -0.0019
   240        0.5136             nan     0.1000   -0.0015
   260        0.5013             nan     0.1000   -0.0020
   280        0.4882             nan     0.1000   -0.0015
   300        0.4760             nan     0.1000   -0.0009
   320        0.4657             nan     0.1000   -0.0013
   340        0.4551             nan     0.1000   -0.0009
   360        0.4450             nan     0.1000   -0.0006
   380        0.4347             nan     0.1000   -0.0010
   400        0.4239             nan     0.1000   -0.0008
   420        0.4142             nan     0.1000   -0.0017
   440        0.4040             nan     0.1000   -0.0008
   460        0.3962             nan     0.1000   -0.0012
   480        0.3898             nan     0.1000   -0.0011
   500        0.3804             nan     0.1000   -0.0008
   520        0.3738             nan     0.1000   -0.0007
   540        0.3688             nan     0.1000   -0.0017
   560        0.3618             nan     0.1000   -0.0007
   580        0.3543             nan     0.1000   -0.0011
   600        0.3484             nan     0.1000   -0.0008
   620        0.3421             nan     0.1000   -0.0007
   640        0.3368             nan     0.1000   -0.0007
   660        0.3308             nan     0.1000   -0.0008
   680        0.3257             nan     0.1000   -0.0005
   700        0.3203             nan     0.1000   -0.0006
   720        0.3152             nan     0.1000   -0.0006
   740        0.3099             nan     0.1000   -0.0003
   760        0.3046             nan     0.1000   -0.0005
   780        0.3016             nan     0.1000   -0.0012
   800        0.2973             nan     0.1000   -0.0023
   820        0.2928             nan     0.1000   -0.0007
   840        0.2886             nan     0.1000   -0.0010
   860        0.2850             nan     0.1000   -0.0005
   880        0.2814             nan     0.1000   -0.0012
   900        0.2783             nan     0.1000   -0.0010
   920        0.2752             nan     0.1000   -0.0019
   940        0.2706             nan     0.1000   -0.0005
   960        0.2673             nan     0.1000   -0.0007
   980        0.2639             nan     0.1000   -0.0010
  1000        0.2612             nan     0.1000   -0.0009
  1020        0.2584             nan     0.1000   -0.0012
  1040        0.2557             nan     0.1000   -0.0018
  1060        0.2521             nan     0.1000   -0.0005
  1080        0.2483             nan     0.1000   -0.0005
  1100        0.2454             nan     0.1000   -0.0008

- Fold01.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0031
     2        1.3199             nan     0.0100    0.0031
     3        1.3138             nan     0.0100    0.0030
     4        1.3082             nan     0.0100    0.0029
     5        1.3033             nan     0.0100    0.0028
     6        1.2972             nan     0.0100    0.0027
     7        1.2916             nan     0.0100    0.0028
     8        1.2857             nan     0.0100    0.0026
     9        1.2808             nan     0.0100    0.0026
    10        1.2753             nan     0.0100    0.0025
    20        1.2281             nan     0.0100    0.0021
    40        1.1544             nan     0.0100    0.0015
    60        1.1038             nan     0.0100    0.0010
    80        1.0664             nan     0.0100    0.0006
   100        1.0353             nan     0.0100    0.0006
   120        1.0100             nan     0.0100    0.0005
   140        0.9895             nan     0.0100    0.0004
   160        0.9713             nan     0.0100    0.0002
   180        0.9559             nan     0.0100    0.0003
   200        0.9431             nan     0.0100    0.0002
   220        0.9316             nan     0.0100    0.0002
   240        0.9214             nan     0.0100    0.0001
   260        0.9126             nan     0.0100    0.0000
   280        0.9048             nan     0.0100    0.0001
   300        0.8971             nan     0.0100    0.0001
   320        0.8908             nan     0.0100    0.0001
   340        0.8847             nan     0.0100    0.0001
   360        0.8788             nan     0.0100   -0.0000
   380        0.8735             nan     0.0100    0.0000
   400        0.8684             nan     0.0100    0.0000
   420        0.8635             nan     0.0100    0.0000
   440        0.8588             nan     0.0100    0.0000
   460        0.8547             nan     0.0100    0.0000
   480        0.8508             nan     0.0100   -0.0000
   500        0.8470             nan     0.0100    0.0001
   520        0.8434             nan     0.0100   -0.0001
   540        0.8396             nan     0.0100   -0.0000
   560        0.8361             nan     0.0100   -0.0000
   580        0.8330             nan     0.0100    0.0000
   600        0.8302             nan     0.0100   -0.0001
   620        0.8275             nan     0.0100   -0.0000
   640        0.8248             nan     0.0100   -0.0001
   660        0.8220             nan     0.0100   -0.0000
   680        0.8198             nan     0.0100   -0.0000
   700        0.8172             nan     0.0100    0.0000
   720        0.8147             nan     0.0100   -0.0001
   740        0.8127             nan     0.0100   -0.0000
   760        0.8106             nan     0.0100   -0.0000
   780        0.8085             nan     0.0100   -0.0001
   800        0.8066             nan     0.0100   -0.0000
   820        0.8046             nan     0.0100   -0.0000
   840        0.8027             nan     0.0100   -0.0001
   860        0.8008             nan     0.0100   -0.0000
   880        0.7989             nan     0.0100   -0.0000
   900        0.7973             nan     0.0100    0.0000
   920        0.7957             nan     0.0100   -0.0001
   940        0.7940             nan     0.0100    0.0000
   960        0.7925             nan     0.0100   -0.0001
   980        0.7911             nan     0.0100   -0.0001
  1000        0.7898             nan     0.0100   -0.0001
  1020        0.7881             nan     0.0100   -0.0000
  1040        0.7867             nan     0.0100   -0.0001
  1060        0.7854             nan     0.0100   -0.0000
  1080        0.7839             nan     0.0100   -0.0000
  1100        0.7824             nan     0.0100   -0.0001

- Fold02.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0037
     2        1.3171             nan     0.0100    0.0033
     3        1.3098             nan     0.0100    0.0035
     4        1.3027             nan     0.0100    0.0036
     5        1.2954             nan     0.0100    0.0035
     6        1.2888             nan     0.0100    0.0031
     7        1.2823             nan     0.0100    0.0030
     8        1.2754             nan     0.0100    0.0031
     9        1.2686             nan     0.0100    0.0031
    10        1.2623             nan     0.0100    0.0031
    20        1.2041             nan     0.0100    0.0024
    40        1.1145             nan     0.0100    0.0019
    60        1.0508             nan     0.0100    0.0013
    80        1.0027             nan     0.0100    0.0010
   100        0.9656             nan     0.0100    0.0007
   120        0.9366             nan     0.0100    0.0006
   140        0.9123             nan     0.0100    0.0003
   160        0.8935             nan     0.0100    0.0003
   180        0.8785             nan     0.0100    0.0003
   200        0.8653             nan     0.0100    0.0002
   220        0.8536             nan     0.0100    0.0001
   240        0.8431             nan     0.0100    0.0002
   260        0.8344             nan     0.0100   -0.0001
   280        0.8265             nan     0.0100    0.0001
   300        0.8197             nan     0.0100    0.0000
   320        0.8121             nan     0.0100    0.0001
   340        0.8056             nan     0.0100   -0.0000
   360        0.7995             nan     0.0100    0.0001
   380        0.7942             nan     0.0100   -0.0000
   400        0.7888             nan     0.0100   -0.0001
   420        0.7834             nan     0.0100   -0.0001
   440        0.7791             nan     0.0100   -0.0001
   460        0.7741             nan     0.0100   -0.0000
   480        0.7703             nan     0.0100   -0.0001
   500        0.7662             nan     0.0100    0.0000
   520        0.7624             nan     0.0100   -0.0002
   540        0.7586             nan     0.0100   -0.0001
   560        0.7554             nan     0.0100   -0.0001
   580        0.7518             nan     0.0100    0.0000
   600        0.7488             nan     0.0100   -0.0000
   620        0.7455             nan     0.0100   -0.0000
   640        0.7426             nan     0.0100   -0.0001
   660        0.7399             nan     0.0100   -0.0000
   680        0.7370             nan     0.0100   -0.0001
   700        0.7340             nan     0.0100   -0.0001
   720        0.7316             nan     0.0100   -0.0001
   740        0.7292             nan     0.0100   -0.0000
   760        0.7265             nan     0.0100   -0.0001
   780        0.7242             nan     0.0100   -0.0001
   800        0.7219             nan     0.0100   -0.0002
   820        0.7197             nan     0.0100   -0.0000
   840        0.7174             nan     0.0100    0.0000
   860        0.7150             nan     0.0100   -0.0001
   880        0.7129             nan     0.0100   -0.0001
   900        0.7106             nan     0.0100   -0.0000
   920        0.7083             nan     0.0100    0.0000
   940        0.7057             nan     0.0100   -0.0001
   960        0.7036             nan     0.0100   -0.0002
   980        0.7014             nan     0.0100    0.0000
  1000        0.6995             nan     0.0100   -0.0001
  1020        0.6976             nan     0.0100   -0.0000
  1040        0.6958             nan     0.0100   -0.0000
  1060        0.6940             nan     0.0100   -0.0002
  1080        0.6917             nan     0.0100   -0.0001
  1100        0.6896             nan     0.0100   -0.0001

- Fold02.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3234             nan     0.0100    0.0040
     2        1.3153             nan     0.0100    0.0037
     3        1.3076             nan     0.0100    0.0034
     4        1.2997             nan     0.0100    0.0040
     5        1.2919             nan     0.0100    0.0034
     6        1.2841             nan     0.0100    0.0037
     7        1.2767             nan     0.0100    0.0037
     8        1.2693             nan     0.0100    0.0036
     9        1.2624             nan     0.0100    0.0034
    10        1.2558             nan     0.0100    0.0034
    20        1.1922             nan     0.0100    0.0029
    40        1.0956             nan     0.0100    0.0017
    60        1.0241             nan     0.0100    0.0015
    80        0.9713             nan     0.0100    0.0009
   100        0.9308             nan     0.0100    0.0008
   120        0.8997             nan     0.0100    0.0006
   140        0.8755             nan     0.0100    0.0004
   160        0.8557             nan     0.0100    0.0002
   180        0.8395             nan     0.0100    0.0003
   200        0.8258             nan     0.0100    0.0001
   220        0.8134             nan     0.0100    0.0001
   240        0.8022             nan     0.0100    0.0002
   260        0.7931             nan     0.0100    0.0000
   280        0.7843             nan     0.0100    0.0001
   300        0.7764             nan     0.0100    0.0001
   320        0.7693             nan     0.0100    0.0001
   340        0.7624             nan     0.0100   -0.0000
   360        0.7561             nan     0.0100   -0.0001
   380        0.7503             nan     0.0100   -0.0000
   400        0.7448             nan     0.0100   -0.0001
   420        0.7393             nan     0.0100   -0.0002
   440        0.7345             nan     0.0100   -0.0000
   460        0.7295             nan     0.0100   -0.0000
   480        0.7245             nan     0.0100   -0.0001
   500        0.7205             nan     0.0100   -0.0000
   520        0.7168             nan     0.0100   -0.0001
   540        0.7126             nan     0.0100   -0.0002
   560        0.7086             nan     0.0100   -0.0001
   580        0.7053             nan     0.0100   -0.0002
   600        0.7014             nan     0.0100   -0.0000
   620        0.6977             nan     0.0100   -0.0001
   640        0.6940             nan     0.0100   -0.0000
   660        0.6905             nan     0.0100   -0.0001
   680        0.6866             nan     0.0100    0.0000
   700        0.6831             nan     0.0100   -0.0001
   720        0.6802             nan     0.0100   -0.0002
   740        0.6767             nan     0.0100   -0.0001
   760        0.6736             nan     0.0100   -0.0001
   780        0.6701             nan     0.0100   -0.0000
   800        0.6675             nan     0.0100   -0.0001
   820        0.6646             nan     0.0100   -0.0002
   840        0.6623             nan     0.0100   -0.0002
   860        0.6595             nan     0.0100   -0.0001
   880        0.6566             nan     0.0100   -0.0001
   900        0.6538             nan     0.0100   -0.0001
   920        0.6510             nan     0.0100   -0.0000
   940        0.6481             nan     0.0100   -0.0001
   960        0.6453             nan     0.0100   -0.0000
   980        0.6426             nan     0.0100   -0.0002
  1000        0.6398             nan     0.0100   -0.0000
  1020        0.6380             nan     0.0100   -0.0001
  1040        0.6355             nan     0.0100   -0.0001
  1060        0.6329             nan     0.0100   -0.0000
  1080        0.6305             nan     0.0100   -0.0001
  1100        0.6279             nan     0.0100   -0.0001

- Fold02.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2715             nan     0.1000    0.0293
     2        1.2178             nan     0.1000    0.0240
     3        1.1785             nan     0.1000    0.0200
     4        1.1445             nan     0.1000    0.0163
     5        1.1184             nan     0.1000    0.0127
     6        1.1047             nan     0.1000    0.0047
     7        1.0831             nan     0.1000    0.0106
     8        1.0610             nan     0.1000    0.0076
     9        1.0440             nan     0.1000    0.0061
    10        1.0309             nan     0.1000    0.0067
    20        0.9405             nan     0.1000    0.0011
    40        0.8679             nan     0.1000    0.0010
    60        0.8294             nan     0.1000   -0.0010
    80        0.8074             nan     0.1000    0.0002
   100        0.7918             nan     0.1000   -0.0001
   120        0.7783             nan     0.1000   -0.0001
   140        0.7680             nan     0.1000   -0.0007
   160        0.7601             nan     0.1000   -0.0011
   180        0.7531             nan     0.1000   -0.0005
   200        0.7472             nan     0.1000   -0.0009
   220        0.7416             nan     0.1000   -0.0010
   240        0.7357             nan     0.1000   -0.0006
   260        0.7280             nan     0.1000   -0.0008
   280        0.7236             nan     0.1000   -0.0004
   300        0.7190             nan     0.1000   -0.0005
   320        0.7146             nan     0.1000   -0.0017
   340        0.7098             nan     0.1000   -0.0009
   360        0.7064             nan     0.1000   -0.0003
   380        0.7044             nan     0.1000   -0.0003
   400        0.7010             nan     0.1000   -0.0009
   420        0.6979             nan     0.1000   -0.0009
   440        0.6952             nan     0.1000   -0.0005
   460        0.6934             nan     0.1000   -0.0008
   480        0.6899             nan     0.1000   -0.0007
   500        0.6879             nan     0.1000   -0.0005
   520        0.6838             nan     0.1000   -0.0003
   540        0.6827             nan     0.1000   -0.0013
   560        0.6800             nan     0.1000   -0.0008
   580        0.6776             nan     0.1000   -0.0009
   600        0.6755             nan     0.1000   -0.0009
   620        0.6734             nan     0.1000   -0.0011
   640        0.6707             nan     0.1000   -0.0005
   660        0.6685             nan     0.1000   -0.0010
   680        0.6670             nan     0.1000   -0.0009
   700        0.6660             nan     0.1000   -0.0007
   720        0.6649             nan     0.1000   -0.0002
   740        0.6621             nan     0.1000   -0.0005
   760        0.6605             nan     0.1000   -0.0007
   780        0.6586             nan     0.1000   -0.0007
   800        0.6573             nan     0.1000   -0.0005
   820        0.6552             nan     0.1000   -0.0011
   840        0.6537             nan     0.1000   -0.0004
   860        0.6513             nan     0.1000   -0.0008
   880        0.6508             nan     0.1000   -0.0005
   900        0.6488             nan     0.1000   -0.0008
   920        0.6470             nan     0.1000   -0.0008
   940        0.6450             nan     0.1000   -0.0008
   960        0.6436             nan     0.1000   -0.0006
   980        0.6421             nan     0.1000   -0.0008
  1000        0.6407             nan     0.1000   -0.0008
  1020        0.6398             nan     0.1000   -0.0013
  1040        0.6382             nan     0.1000   -0.0003
  1060        0.6370             nan     0.1000   -0.0010
  1080        0.6358             nan     0.1000   -0.0006
  1100        0.6349             nan     0.1000   -0.0004

- Fold02.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2580             nan     0.1000    0.0343
     2        1.1981             nan     0.1000    0.0271
     3        1.1509             nan     0.1000    0.0258
     4        1.1104             nan     0.1000    0.0198
     5        1.0732             nan     0.1000    0.0182
     6        1.0435             nan     0.1000    0.0147
     7        1.0181             nan     0.1000    0.0128
     8        0.9926             nan     0.1000    0.0112
     9        0.9730             nan     0.1000    0.0087
    10        0.9527             nan     0.1000    0.0078
    20        0.8607             nan     0.1000    0.0024
    40        0.7888             nan     0.1000   -0.0005
    60        0.7490             nan     0.1000   -0.0009
    80        0.7231             nan     0.1000   -0.0007
   100        0.6995             nan     0.1000   -0.0016
   120        0.6803             nan     0.1000   -0.0003
   140        0.6638             nan     0.1000   -0.0010
   160        0.6486             nan     0.1000   -0.0008
   180        0.6339             nan     0.1000   -0.0005
   200        0.6247             nan     0.1000   -0.0008
   220        0.6143             nan     0.1000   -0.0004
   240        0.6053             nan     0.1000   -0.0018
   260        0.5944             nan     0.1000   -0.0004
   280        0.5841             nan     0.1000   -0.0001
   300        0.5751             nan     0.1000   -0.0020
   320        0.5660             nan     0.1000   -0.0005
   340        0.5608             nan     0.1000   -0.0006
   360        0.5521             nan     0.1000   -0.0012
   380        0.5472             nan     0.1000   -0.0010
   400        0.5414             nan     0.1000   -0.0011
   420        0.5350             nan     0.1000   -0.0007
   440        0.5274             nan     0.1000   -0.0016
   460        0.5229             nan     0.1000   -0.0002
   480        0.5168             nan     0.1000   -0.0010
   500        0.5089             nan     0.1000   -0.0009
   520        0.5014             nan     0.1000   -0.0010
   540        0.4980             nan     0.1000   -0.0008
   560        0.4921             nan     0.1000   -0.0008
   580        0.4862             nan     0.1000   -0.0009
   600        0.4815             nan     0.1000   -0.0012
   620        0.4767             nan     0.1000   -0.0012
   640        0.4716             nan     0.1000   -0.0006
   660        0.4664             nan     0.1000   -0.0009
   680        0.4604             nan     0.1000   -0.0003
   700        0.4561             nan     0.1000   -0.0009
   720        0.4520             nan     0.1000   -0.0009
   740        0.4472             nan     0.1000   -0.0005
   760        0.4433             nan     0.1000   -0.0013
   780        0.4396             nan     0.1000   -0.0008
   800        0.4366             nan     0.1000   -0.0010
   820        0.4336             nan     0.1000   -0.0011
   840        0.4315             nan     0.1000   -0.0004
   860        0.4291             nan     0.1000   -0.0006
   880        0.4248             nan     0.1000   -0.0007
   900        0.4207             nan     0.1000   -0.0008
   920        0.4168             nan     0.1000   -0.0010
   940        0.4130             nan     0.1000   -0.0009
   960        0.4088             nan     0.1000   -0.0007
   980        0.4062             nan     0.1000   -0.0016
  1000        0.4034             nan     0.1000   -0.0013
  1020        0.3999             nan     0.1000   -0.0006
  1040        0.3960             nan     0.1000   -0.0005
  1060        0.3923             nan     0.1000   -0.0010
  1080        0.3890             nan     0.1000   -0.0009
  1100        0.3862             nan     0.1000   -0.0008

- Fold02.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2503             nan     0.1000    0.0389
     2        1.1832             nan     0.1000    0.0313
     3        1.1333             nan     0.1000    0.0239
     4        1.0852             nan     0.1000    0.0224
     5        1.0467             nan     0.1000    0.0191
     6        1.0165             nan     0.1000    0.0144
     7        0.9906             nan     0.1000    0.0112
     8        0.9632             nan     0.1000    0.0117
     9        0.9416             nan     0.1000    0.0096
    10        0.9248             nan     0.1000    0.0064
    20        0.8220             nan     0.1000    0.0013
    40        0.7429             nan     0.1000   -0.0009
    60        0.7061             nan     0.1000   -0.0016
    80        0.6771             nan     0.1000   -0.0024
   100        0.6510             nan     0.1000   -0.0002
   120        0.6283             nan     0.1000    0.0003
   140        0.6048             nan     0.1000   -0.0010
   160        0.5845             nan     0.1000   -0.0012
   180        0.5641             nan     0.1000   -0.0010
   200        0.5518             nan     0.1000   -0.0018
   220        0.5366             nan     0.1000   -0.0021
   240        0.5272             nan     0.1000   -0.0018
   260        0.5186             nan     0.1000   -0.0005
   280        0.5075             nan     0.1000   -0.0017
   300        0.4933             nan     0.1000   -0.0014
   320        0.4829             nan     0.1000   -0.0009
   340        0.4744             nan     0.1000   -0.0015
   360        0.4626             nan     0.1000   -0.0009
   380        0.4512             nan     0.1000   -0.0005
   400        0.4413             nan     0.1000   -0.0012
   420        0.4357             nan     0.1000   -0.0014
   440        0.4262             nan     0.1000   -0.0006
   460        0.4155             nan     0.1000   -0.0015
   480        0.4071             nan     0.1000   -0.0008
   500        0.4003             nan     0.1000   -0.0018
   520        0.3916             nan     0.1000   -0.0015
   540        0.3866             nan     0.1000   -0.0006
   560        0.3803             nan     0.1000   -0.0010
   580        0.3725             nan     0.1000   -0.0013
   600        0.3662             nan     0.1000   -0.0007
   620        0.3587             nan     0.1000   -0.0013
   640        0.3540             nan     0.1000   -0.0015
   660        0.3487             nan     0.1000   -0.0008
   680        0.3438             nan     0.1000   -0.0008
   700        0.3375             nan     0.1000   -0.0007
   720        0.3336             nan     0.1000   -0.0011
   740        0.3290             nan     0.1000   -0.0004
   760        0.3233             nan     0.1000   -0.0009
   780        0.3186             nan     0.1000   -0.0008
   800        0.3146             nan     0.1000   -0.0011
   820        0.3105             nan     0.1000   -0.0010
   840        0.3061             nan     0.1000   -0.0008
   860        0.3010             nan     0.1000   -0.0008
   880        0.2963             nan     0.1000   -0.0014
   900        0.2923             nan     0.1000   -0.0009
   920        0.2878             nan     0.1000   -0.0008
   940        0.2842             nan     0.1000   -0.0007
   960        0.2792             nan     0.1000   -0.0010
   980        0.2755             nan     0.1000   -0.0008
  1000        0.2725             nan     0.1000   -0.0010
  1020        0.2688             nan     0.1000   -0.0006
  1040        0.2663             nan     0.1000   -0.0004
  1060        0.2620             nan     0.1000   -0.0001
  1080        0.2582             nan     0.1000   -0.0007
  1100        0.2543             nan     0.1000   -0.0007

- Fold02.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3263             nan     0.0100    0.0029
     2        1.3205             nan     0.0100    0.0028
     3        1.3148             nan     0.0100    0.0027
     4        1.3093             nan     0.0100    0.0026
     5        1.3041             nan     0.0100    0.0027
     6        1.2988             nan     0.0100    0.0026
     7        1.2938             nan     0.0100    0.0026
     8        1.2885             nan     0.0100    0.0025
     9        1.2839             nan     0.0100    0.0025
    10        1.2795             nan     0.0100    0.0024
    20        1.2350             nan     0.0100    0.0020
    40        1.1668             nan     0.0100    0.0013
    60        1.1214             nan     0.0100    0.0004
    80        1.0868             nan     0.0100    0.0007
   100        1.0590             nan     0.0100    0.0005
   120        1.0366             nan     0.0100    0.0004
   140        1.0173             nan     0.0100    0.0003
   160        1.0015             nan     0.0100    0.0003
   180        0.9877             nan     0.0100    0.0002
   200        0.9754             nan     0.0100    0.0003
   220        0.9644             nan     0.0100    0.0002
   240        0.9549             nan     0.0100    0.0002
   260        0.9465             nan     0.0100    0.0002
   280        0.9384             nan     0.0100    0.0001
   300        0.9312             nan     0.0100    0.0001
   320        0.9245             nan     0.0100    0.0001
   340        0.9178             nan     0.0100    0.0001
   360        0.9122             nan     0.0100    0.0000
   380        0.9070             nan     0.0100    0.0001
   400        0.9020             nan     0.0100    0.0001
   420        0.8977             nan     0.0100    0.0001
   440        0.8938             nan     0.0100    0.0001
   460        0.8895             nan     0.0100    0.0000
   480        0.8859             nan     0.0100   -0.0000
   500        0.8823             nan     0.0100    0.0000
   520        0.8787             nan     0.0100    0.0000
   540        0.8753             nan     0.0100   -0.0000
   560        0.8722             nan     0.0100   -0.0001
   580        0.8692             nan     0.0100    0.0000
   600        0.8662             nan     0.0100    0.0000
   620        0.8637             nan     0.0100   -0.0001
   640        0.8611             nan     0.0100   -0.0000
   660        0.8586             nan     0.0100    0.0000
   680        0.8562             nan     0.0100   -0.0000
   700        0.8539             nan     0.0100    0.0000
   720        0.8514             nan     0.0100   -0.0000
   740        0.8490             nan     0.0100   -0.0000
   760        0.8467             nan     0.0100   -0.0001
   780        0.8444             nan     0.0100   -0.0000
   800        0.8424             nan     0.0100    0.0000
   820        0.8403             nan     0.0100   -0.0000
   840        0.8384             nan     0.0100   -0.0000
   860        0.8366             nan     0.0100   -0.0000
   880        0.8348             nan     0.0100   -0.0000
   900        0.8333             nan     0.0100   -0.0000
   920        0.8317             nan     0.0100   -0.0001
   940        0.8299             nan     0.0100   -0.0000
   960        0.8283             nan     0.0100   -0.0003
   980        0.8268             nan     0.0100   -0.0000
  1000        0.8251             nan     0.0100   -0.0001
  1020        0.8237             nan     0.0100   -0.0001
  1040        0.8222             nan     0.0100   -0.0000
  1060        0.8209             nan     0.0100   -0.0000
  1080        0.8195             nan     0.0100   -0.0001
  1100        0.8180             nan     0.0100   -0.0000

- Fold03.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0034
     2        1.3182             nan     0.0100    0.0034
     3        1.3109             nan     0.0100    0.0037
     4        1.3042             nan     0.0100    0.0036
     5        1.2976             nan     0.0100    0.0031
     6        1.2907             nan     0.0100    0.0031
     7        1.2839             nan     0.0100    0.0031
     8        1.2779             nan     0.0100    0.0033
     9        1.2715             nan     0.0100    0.0029
    10        1.2655             nan     0.0100    0.0030
    20        1.2114             nan     0.0100    0.0024
    40        1.1256             nan     0.0100    0.0016
    60        1.0645             nan     0.0100    0.0012
    80        1.0197             nan     0.0100    0.0009
   100        0.9856             nan     0.0100    0.0007
   120        0.9584             nan     0.0100    0.0005
   140        0.9369             nan     0.0100    0.0004
   160        0.9198             nan     0.0100    0.0002
   180        0.9054             nan     0.0100    0.0001
   200        0.8937             nan     0.0100    0.0000
   220        0.8834             nan     0.0100    0.0000
   240        0.8747             nan     0.0100   -0.0001
   260        0.8662             nan     0.0100    0.0001
   280        0.8579             nan     0.0100   -0.0000
   300        0.8505             nan     0.0100    0.0002
   320        0.8439             nan     0.0100   -0.0000
   340        0.8378             nan     0.0100    0.0001
   360        0.8317             nan     0.0100    0.0001
   380        0.8262             nan     0.0100    0.0000
   400        0.8207             nan     0.0100    0.0000
   420        0.8155             nan     0.0100   -0.0000
   440        0.8111             nan     0.0100   -0.0000
   460        0.8068             nan     0.0100    0.0000
   480        0.8020             nan     0.0100   -0.0001
   500        0.7982             nan     0.0100   -0.0001
   520        0.7944             nan     0.0100   -0.0001
   540        0.7907             nan     0.0100    0.0000
   560        0.7875             nan     0.0100   -0.0001
   580        0.7843             nan     0.0100   -0.0001
   600        0.7811             nan     0.0100   -0.0000
   620        0.7782             nan     0.0100   -0.0001
   640        0.7750             nan     0.0100   -0.0001
   660        0.7723             nan     0.0100    0.0000
   680        0.7694             nan     0.0100   -0.0001
   700        0.7670             nan     0.0100   -0.0000
   720        0.7646             nan     0.0100   -0.0000
   740        0.7617             nan     0.0100   -0.0001
   760        0.7590             nan     0.0100   -0.0001
   780        0.7565             nan     0.0100   -0.0001
   800        0.7538             nan     0.0100   -0.0001
   820        0.7514             nan     0.0100   -0.0001
   840        0.7492             nan     0.0100   -0.0000
   860        0.7467             nan     0.0100   -0.0000
   880        0.7442             nan     0.0100   -0.0001
   900        0.7420             nan     0.0100   -0.0000
   920        0.7399             nan     0.0100   -0.0001
   940        0.7375             nan     0.0100   -0.0000
   960        0.7358             nan     0.0100   -0.0001
   980        0.7341             nan     0.0100   -0.0001
  1000        0.7317             nan     0.0100   -0.0000
  1020        0.7296             nan     0.0100   -0.0001
  1040        0.7275             nan     0.0100   -0.0002
  1060        0.7253             nan     0.0100   -0.0001
  1080        0.7237             nan     0.0100   -0.0001
  1100        0.7217             nan     0.0100   -0.0001

- Fold03.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0037
     2        1.3168             nan     0.0100    0.0035
     3        1.3095             nan     0.0100    0.0037
     4        1.3026             nan     0.0100    0.0036
     5        1.2954             nan     0.0100    0.0034
     6        1.2885             nan     0.0100    0.0034
     7        1.2813             nan     0.0100    0.0033
     8        1.2750             nan     0.0100    0.0035
     9        1.2686             nan     0.0100    0.0032
    10        1.2624             nan     0.0100    0.0028
    20        1.2032             nan     0.0100    0.0026
    40        1.1116             nan     0.0100    0.0018
    60        1.0438             nan     0.0100    0.0013
    80        0.9936             nan     0.0100    0.0008
   100        0.9553             nan     0.0100    0.0007
   120        0.9260             nan     0.0100    0.0006
   140        0.9035             nan     0.0100    0.0003
   160        0.8845             nan     0.0100    0.0002
   180        0.8690             nan     0.0100    0.0002
   200        0.8546             nan     0.0100    0.0001
   220        0.8423             nan     0.0100    0.0001
   240        0.8312             nan     0.0100   -0.0001
   260        0.8214             nan     0.0100    0.0001
   280        0.8132             nan     0.0100   -0.0000
   300        0.8059             nan     0.0100   -0.0001
   320        0.7991             nan     0.0100    0.0000
   340        0.7923             nan     0.0100   -0.0000
   360        0.7865             nan     0.0100    0.0000
   380        0.7807             nan     0.0100    0.0001
   400        0.7747             nan     0.0100    0.0000
   420        0.7692             nan     0.0100   -0.0001
   440        0.7643             nan     0.0100    0.0000
   460        0.7593             nan     0.0100   -0.0000
   480        0.7547             nan     0.0100   -0.0000
   500        0.7502             nan     0.0100   -0.0001
   520        0.7459             nan     0.0100   -0.0000
   540        0.7421             nan     0.0100   -0.0001
   560        0.7376             nan     0.0100   -0.0001
   580        0.7339             nan     0.0100   -0.0001
   600        0.7298             nan     0.0100   -0.0001
   620        0.7257             nan     0.0100   -0.0000
   640        0.7222             nan     0.0100   -0.0000
   660        0.7183             nan     0.0100   -0.0001
   680        0.7152             nan     0.0100   -0.0000
   700        0.7116             nan     0.0100   -0.0001
   720        0.7082             nan     0.0100   -0.0001
   740        0.7053             nan     0.0100   -0.0001
   760        0.7023             nan     0.0100   -0.0001
   780        0.6995             nan     0.0100   -0.0001
   800        0.6968             nan     0.0100   -0.0001
   820        0.6938             nan     0.0100   -0.0002
   840        0.6907             nan     0.0100   -0.0000
   860        0.6878             nan     0.0100   -0.0000
   880        0.6849             nan     0.0100   -0.0001
   900        0.6827             nan     0.0100   -0.0001
   920        0.6796             nan     0.0100   -0.0001
   940        0.6772             nan     0.0100   -0.0001
   960        0.6743             nan     0.0100   -0.0001
   980        0.6716             nan     0.0100   -0.0002
  1000        0.6686             nan     0.0100   -0.0000
  1020        0.6658             nan     0.0100   -0.0002
  1040        0.6638             nan     0.0100   -0.0001
  1060        0.6612             nan     0.0100   -0.0001
  1080        0.6584             nan     0.0100   -0.0000
  1100        0.6556             nan     0.0100   -0.0001

- Fold03.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2752             nan     0.1000    0.0262
     2        1.2262             nan     0.1000    0.0198
     3        1.1894             nan     0.1000    0.0185
     4        1.1555             nan     0.1000    0.0146
     5        1.1314             nan     0.1000    0.0120
     6        1.1097             nan     0.1000    0.0098
     7        1.0927             nan     0.1000    0.0063
     8        1.0777             nan     0.1000    0.0079
     9        1.0662             nan     0.1000    0.0066
    10        1.0544             nan     0.1000    0.0052
    20        0.9740             nan     0.1000    0.0029
    40        0.9033             nan     0.1000    0.0011
    60        0.8682             nan     0.1000   -0.0002
    80        0.8437             nan     0.1000    0.0003
   100        0.8261             nan     0.1000   -0.0004
   120        0.8154             nan     0.1000   -0.0004
   140        0.8021             nan     0.1000   -0.0004
   160        0.7945             nan     0.1000   -0.0004
   180        0.7885             nan     0.1000   -0.0007
   200        0.7807             nan     0.1000   -0.0014
   220        0.7732             nan     0.1000   -0.0011
   240        0.7665             nan     0.1000   -0.0011
   260        0.7610             nan     0.1000   -0.0001
   280        0.7549             nan     0.1000   -0.0008
   300        0.7517             nan     0.1000   -0.0009
   320        0.7466             nan     0.1000   -0.0003
   340        0.7439             nan     0.1000   -0.0004
   360        0.7390             nan     0.1000   -0.0011
   380        0.7369             nan     0.1000   -0.0003
   400        0.7335             nan     0.1000   -0.0005
   420        0.7286             nan     0.1000   -0.0011
   440        0.7254             nan     0.1000   -0.0004
   460        0.7208             nan     0.1000   -0.0004
   480        0.7173             nan     0.1000   -0.0005
   500        0.7156             nan     0.1000   -0.0007
   520        0.7139             nan     0.1000   -0.0005
   540        0.7117             nan     0.1000   -0.0009
   560        0.7094             nan     0.1000   -0.0007
   580        0.7060             nan     0.1000   -0.0004
   600        0.7040             nan     0.1000   -0.0006
   620        0.7012             nan     0.1000   -0.0005
   640        0.7002             nan     0.1000   -0.0007
   660        0.6988             nan     0.1000   -0.0003
   680        0.6969             nan     0.1000   -0.0008
   700        0.6952             nan     0.1000   -0.0009
   720        0.6929             nan     0.1000   -0.0004
   740        0.6910             nan     0.1000   -0.0007
   760        0.6886             nan     0.1000   -0.0010
   780        0.6862             nan     0.1000   -0.0007
   800        0.6841             nan     0.1000   -0.0004
   820        0.6809             nan     0.1000   -0.0003
   840        0.6797             nan     0.1000   -0.0011
   860        0.6787             nan     0.1000   -0.0010
   880        0.6761             nan     0.1000   -0.0006
   900        0.6740             nan     0.1000   -0.0007
   920        0.6718             nan     0.1000   -0.0012
   940        0.6699             nan     0.1000   -0.0005
   960        0.6682             nan     0.1000   -0.0017
   980        0.6677             nan     0.1000   -0.0009
  1000        0.6657             nan     0.1000   -0.0003
  1020        0.6639             nan     0.1000   -0.0007
  1040        0.6627             nan     0.1000   -0.0008
  1060        0.6610             nan     0.1000   -0.0006
  1080        0.6587             nan     0.1000   -0.0005
  1100        0.6566             nan     0.1000   -0.0007

- Fold03.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2627             nan     0.1000    0.0318
     2        1.2040             nan     0.1000    0.0265
     3        1.1628             nan     0.1000    0.0221
     4        1.1191             nan     0.1000    0.0189
     5        1.0863             nan     0.1000    0.0162
     6        1.0588             nan     0.1000    0.0129
     7        1.0358             nan     0.1000    0.0111
     8        1.0127             nan     0.1000    0.0095
     9        0.9945             nan     0.1000    0.0082
    10        0.9786             nan     0.1000    0.0072
    20        0.8917             nan     0.1000    0.0017
    40        0.8193             nan     0.1000   -0.0005
    60        0.7848             nan     0.1000   -0.0009
    80        0.7565             nan     0.1000   -0.0009
   100        0.7296             nan     0.1000   -0.0018
   120        0.7143             nan     0.1000   -0.0005
   140        0.6983             nan     0.1000   -0.0007
   160        0.6808             nan     0.1000   -0.0002
   180        0.6661             nan     0.1000   -0.0009
   200        0.6542             nan     0.1000   -0.0009
   220        0.6403             nan     0.1000   -0.0013
   240        0.6308             nan     0.1000   -0.0011
   260        0.6222             nan     0.1000   -0.0013
   280        0.6148             nan     0.1000   -0.0003
   300        0.6038             nan     0.1000   -0.0013
   320        0.5950             nan     0.1000   -0.0007
   340        0.5846             nan     0.1000   -0.0010
   360        0.5772             nan     0.1000   -0.0009
   380        0.5676             nan     0.1000   -0.0009
   400        0.5578             nan     0.1000   -0.0009
   420        0.5497             nan     0.1000   -0.0005
   440        0.5459             nan     0.1000   -0.0011
   460        0.5393             nan     0.1000   -0.0009
   480        0.5326             nan     0.1000   -0.0009
   500        0.5265             nan     0.1000   -0.0007
   520        0.5223             nan     0.1000   -0.0016
   540        0.5182             nan     0.1000   -0.0016
   560        0.5110             nan     0.1000   -0.0008
   580        0.5061             nan     0.1000   -0.0007
   600        0.5020             nan     0.1000   -0.0012
   620        0.4970             nan     0.1000   -0.0012
   640        0.4911             nan     0.1000   -0.0012
   660        0.4862             nan     0.1000   -0.0002
   680        0.4792             nan     0.1000   -0.0013
   700        0.4775             nan     0.1000   -0.0009
   720        0.4720             nan     0.1000   -0.0006
   740        0.4662             nan     0.1000   -0.0008
   760        0.4625             nan     0.1000   -0.0012
   780        0.4582             nan     0.1000   -0.0009
   800        0.4541             nan     0.1000   -0.0004
   820        0.4510             nan     0.1000   -0.0008
   840        0.4470             nan     0.1000   -0.0003
   860        0.4436             nan     0.1000   -0.0008
   880        0.4403             nan     0.1000   -0.0005
   900        0.4367             nan     0.1000   -0.0012
   920        0.4331             nan     0.1000   -0.0010
   940        0.4292             nan     0.1000   -0.0010
   960        0.4254             nan     0.1000   -0.0004
   980        0.4202             nan     0.1000   -0.0007
  1000        0.4162             nan     0.1000   -0.0006
  1020        0.4147             nan     0.1000   -0.0016
  1040        0.4114             nan     0.1000   -0.0011
  1060        0.4086             nan     0.1000   -0.0007
  1080        0.4048             nan     0.1000   -0.0007
  1100        0.4037             nan     0.1000   -0.0007

- Fold03.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2593             nan     0.1000    0.0329
     2        1.2000             nan     0.1000    0.0308
     3        1.1549             nan     0.1000    0.0219
     4        1.1085             nan     0.1000    0.0213
     5        1.0720             nan     0.1000    0.0189
     6        1.0430             nan     0.1000    0.0129
     7        1.0130             nan     0.1000    0.0131
     8        0.9900             nan     0.1000    0.0094
     9        0.9712             nan     0.1000    0.0071
    10        0.9533             nan     0.1000    0.0076
    20        0.8558             nan     0.1000    0.0007
    40        0.7773             nan     0.1000   -0.0009
    60        0.7394             nan     0.1000   -0.0008
    80        0.7080             nan     0.1000   -0.0017
   100        0.6746             nan     0.1000   -0.0002
   120        0.6486             nan     0.1000   -0.0018
   140        0.6284             nan     0.1000   -0.0007
   160        0.6115             nan     0.1000    0.0003
   180        0.5935             nan     0.1000   -0.0017
   200        0.5771             nan     0.1000   -0.0007
   220        0.5623             nan     0.1000   -0.0011
   240        0.5463             nan     0.1000   -0.0015
   260        0.5322             nan     0.1000   -0.0011
   280        0.5192             nan     0.1000   -0.0009
   300        0.5073             nan     0.1000   -0.0011
   320        0.4977             nan     0.1000   -0.0018
   340        0.4882             nan     0.1000   -0.0008
   360        0.4783             nan     0.1000   -0.0018
   380        0.4688             nan     0.1000   -0.0007
   400        0.4617             nan     0.1000   -0.0011
   420        0.4512             nan     0.1000   -0.0009
   440        0.4441             nan     0.1000   -0.0009
   460        0.4359             nan     0.1000   -0.0015
   480        0.4268             nan     0.1000   -0.0011
   500        0.4197             nan     0.1000   -0.0006
   520        0.4128             nan     0.1000   -0.0008
   540        0.4044             nan     0.1000   -0.0009
   560        0.3977             nan     0.1000   -0.0012
   580        0.3909             nan     0.1000   -0.0007
   600        0.3849             nan     0.1000   -0.0006
   620        0.3790             nan     0.1000   -0.0005
   640        0.3739             nan     0.1000   -0.0005
   660        0.3673             nan     0.1000   -0.0009
   680        0.3627             nan     0.1000   -0.0009
   700        0.3551             nan     0.1000   -0.0010
   720        0.3503             nan     0.1000   -0.0011
   740        0.3454             nan     0.1000   -0.0010
   760        0.3404             nan     0.1000   -0.0005
   780        0.3356             nan     0.1000   -0.0013
   800        0.3290             nan     0.1000   -0.0008
   820        0.3236             nan     0.1000   -0.0003
   840        0.3197             nan     0.1000   -0.0005
   860        0.3150             nan     0.1000   -0.0006
   880        0.3108             nan     0.1000   -0.0009
   900        0.3075             nan     0.1000   -0.0009
   920        0.3044             nan     0.1000   -0.0006
   940        0.3007             nan     0.1000   -0.0008
   960        0.2954             nan     0.1000   -0.0004
   980        0.2922             nan     0.1000   -0.0014
  1000        0.2878             nan     0.1000   -0.0014
  1020        0.2824             nan     0.1000   -0.0015
  1040        0.2785             nan     0.1000   -0.0008
  1060        0.2736             nan     0.1000   -0.0008
  1080        0.2709             nan     0.1000   -0.0006
  1100        0.2681             nan     0.1000   -0.0008

- Fold03.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3251             nan     0.0100    0.0031
     2        1.3193             nan     0.0100    0.0029
     3        1.3128             nan     0.0100    0.0029
     4        1.3069             nan     0.0100    0.0030
     5        1.3009             nan     0.0100    0.0028
     6        1.2952             nan     0.0100    0.0027
     7        1.2896             nan     0.0100    0.0026
     8        1.2842             nan     0.0100    0.0027
     9        1.2792             nan     0.0100    0.0026
    10        1.2740             nan     0.0100    0.0025
    20        1.2270             nan     0.0100    0.0021
    40        1.1558             nan     0.0100    0.0015
    60        1.1062             nan     0.0100    0.0011
    80        1.0664             nan     0.0100    0.0008
   100        1.0357             nan     0.0100    0.0006
   120        1.0100             nan     0.0100    0.0005
   140        0.9882             nan     0.0100    0.0004
   160        0.9699             nan     0.0100    0.0004
   180        0.9537             nan     0.0100    0.0003
   200        0.9394             nan     0.0100    0.0003
   220        0.9261             nan     0.0100    0.0002
   240        0.9144             nan     0.0100    0.0002
   260        0.9034             nan     0.0100    0.0002
   280        0.8946             nan     0.0100    0.0001
   300        0.8863             nan     0.0100    0.0002
   320        0.8791             nan     0.0100    0.0001
   340        0.8721             nan     0.0100    0.0001
   360        0.8653             nan     0.0100    0.0001
   380        0.8599             nan     0.0100    0.0001
   400        0.8545             nan     0.0100    0.0001
   420        0.8494             nan     0.0100   -0.0001
   440        0.8443             nan     0.0100    0.0000
   460        0.8403             nan     0.0100   -0.0000
   480        0.8363             nan     0.0100   -0.0000
   500        0.8324             nan     0.0100    0.0000
   520        0.8286             nan     0.0100    0.0000
   540        0.8253             nan     0.0100    0.0000
   560        0.8222             nan     0.0100   -0.0001
   580        0.8188             nan     0.0100    0.0000
   600        0.8157             nan     0.0100   -0.0001
   620        0.8124             nan     0.0100    0.0000
   640        0.8098             nan     0.0100   -0.0001
   660        0.8068             nan     0.0100    0.0000
   680        0.8038             nan     0.0100   -0.0000
   700        0.8014             nan     0.0100    0.0000
   720        0.7987             nan     0.0100   -0.0000
   740        0.7965             nan     0.0100   -0.0000
   760        0.7942             nan     0.0100   -0.0001
   780        0.7918             nan     0.0100    0.0000
   800        0.7894             nan     0.0100   -0.0000
   820        0.7871             nan     0.0100   -0.0001
   840        0.7852             nan     0.0100    0.0000
   860        0.7831             nan     0.0100   -0.0000
   880        0.7812             nan     0.0100   -0.0000
   900        0.7793             nan     0.0100   -0.0000
   920        0.7773             nan     0.0100   -0.0000
   940        0.7755             nan     0.0100    0.0000
   960        0.7738             nan     0.0100   -0.0000
   980        0.7721             nan     0.0100   -0.0001
  1000        0.7705             nan     0.0100    0.0000
  1020        0.7689             nan     0.0100   -0.0001
  1040        0.7673             nan     0.0100   -0.0000
  1060        0.7660             nan     0.0100   -0.0001
  1080        0.7645             nan     0.0100   -0.0000
  1100        0.7630             nan     0.0100   -0.0000

- Fold04.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0040
     2        1.3155             nan     0.0100    0.0038
     3        1.3078             nan     0.0100    0.0037
     4        1.2998             nan     0.0100    0.0033
     5        1.2931             nan     0.0100    0.0035
     6        1.2860             nan     0.0100    0.0032
     7        1.2789             nan     0.0100    0.0033
     8        1.2723             nan     0.0100    0.0032
     9        1.2652             nan     0.0100    0.0033
    10        1.2587             nan     0.0100    0.0035
    20        1.1968             nan     0.0100    0.0025
    40        1.1038             nan     0.0100    0.0017
    60        1.0373             nan     0.0100    0.0014
    80        0.9864             nan     0.0100    0.0008
   100        0.9480             nan     0.0100    0.0008
   120        0.9175             nan     0.0100    0.0004
   140        0.8932             nan     0.0100    0.0003
   160        0.8748             nan     0.0100    0.0004
   180        0.8592             nan     0.0100    0.0003
   200        0.8459             nan     0.0100    0.0001
   220        0.8344             nan     0.0100   -0.0000
   240        0.8235             nan     0.0100    0.0000
   260        0.8142             nan     0.0100    0.0001
   280        0.8065             nan     0.0100    0.0002
   300        0.7984             nan     0.0100    0.0001
   320        0.7913             nan     0.0100   -0.0000
   340        0.7840             nan     0.0100    0.0000
   360        0.7780             nan     0.0100   -0.0000
   380        0.7728             nan     0.0100    0.0001
   400        0.7674             nan     0.0100   -0.0001
   420        0.7629             nan     0.0100   -0.0000
   440        0.7584             nan     0.0100    0.0000
   460        0.7535             nan     0.0100    0.0000
   480        0.7495             nan     0.0100   -0.0000
   500        0.7456             nan     0.0100   -0.0001
   520        0.7419             nan     0.0100   -0.0001
   540        0.7380             nan     0.0100   -0.0001
   560        0.7345             nan     0.0100   -0.0000
   580        0.7309             nan     0.0100   -0.0001
   600        0.7277             nan     0.0100    0.0000
   620        0.7245             nan     0.0100   -0.0001
   640        0.7215             nan     0.0100    0.0000
   660        0.7190             nan     0.0100   -0.0000
   680        0.7161             nan     0.0100   -0.0001
   700        0.7132             nan     0.0100   -0.0001
   720        0.7104             nan     0.0100   -0.0001
   740        0.7076             nan     0.0100   -0.0001
   760        0.7051             nan     0.0100   -0.0002
   780        0.7026             nan     0.0100   -0.0001
   800        0.7001             nan     0.0100   -0.0002
   820        0.6979             nan     0.0100   -0.0001
   840        0.6957             nan     0.0100   -0.0000
   860        0.6934             nan     0.0100    0.0000
   880        0.6915             nan     0.0100   -0.0001
   900        0.6892             nan     0.0100   -0.0002
   920        0.6870             nan     0.0100   -0.0000
   940        0.6849             nan     0.0100   -0.0001
   960        0.6828             nan     0.0100   -0.0001
   980        0.6811             nan     0.0100   -0.0001
  1000        0.6793             nan     0.0100   -0.0001
  1020        0.6774             nan     0.0100   -0.0000
  1040        0.6758             nan     0.0100   -0.0000
  1060        0.6739             nan     0.0100   -0.0001
  1080        0.6720             nan     0.0100   -0.0001
  1100        0.6703             nan     0.0100   -0.0001

- Fold04.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3228             nan     0.0100    0.0043
     2        1.3148             nan     0.0100    0.0042
     3        1.3063             nan     0.0100    0.0039
     4        1.2986             nan     0.0100    0.0038
     5        1.2910             nan     0.0100    0.0041
     6        1.2833             nan     0.0100    0.0039
     7        1.2753             nan     0.0100    0.0037
     8        1.2681             nan     0.0100    0.0038
     9        1.2609             nan     0.0100    0.0037
    10        1.2538             nan     0.0100    0.0036
    20        1.1873             nan     0.0100    0.0027
    40        1.0863             nan     0.0100    0.0021
    60        1.0120             nan     0.0100    0.0015
    80        0.9579             nan     0.0100    0.0011
   100        0.9146             nan     0.0100    0.0009
   120        0.8814             nan     0.0100    0.0005
   140        0.8564             nan     0.0100    0.0002
   160        0.8368             nan     0.0100    0.0003
   180        0.8199             nan     0.0100    0.0004
   200        0.8054             nan     0.0100    0.0001
   220        0.7932             nan     0.0100   -0.0000
   240        0.7827             nan     0.0100   -0.0000
   260        0.7731             nan     0.0100   -0.0000
   280        0.7640             nan     0.0100    0.0000
   300        0.7565             nan     0.0100    0.0000
   320        0.7489             nan     0.0100   -0.0001
   340        0.7411             nan     0.0100    0.0000
   360        0.7344             nan     0.0100   -0.0000
   380        0.7280             nan     0.0100   -0.0000
   400        0.7222             nan     0.0100    0.0000
   420        0.7174             nan     0.0100    0.0000
   440        0.7123             nan     0.0100   -0.0000
   460        0.7073             nan     0.0100   -0.0000
   480        0.7026             nan     0.0100    0.0000
   500        0.6979             nan     0.0100   -0.0001
   520        0.6936             nan     0.0100   -0.0000
   540        0.6893             nan     0.0100   -0.0001
   560        0.6860             nan     0.0100   -0.0001
   580        0.6823             nan     0.0100   -0.0001
   600        0.6783             nan     0.0100   -0.0001
   620        0.6743             nan     0.0100   -0.0001
   640        0.6709             nan     0.0100    0.0000
   660        0.6674             nan     0.0100   -0.0000
   680        0.6642             nan     0.0100   -0.0001
   700        0.6617             nan     0.0100   -0.0002
   720        0.6586             nan     0.0100   -0.0002
   740        0.6555             nan     0.0100   -0.0000
   760        0.6528             nan     0.0100   -0.0001
   780        0.6499             nan     0.0100   -0.0001
   800        0.6473             nan     0.0100   -0.0001
   820        0.6445             nan     0.0100   -0.0001
   840        0.6415             nan     0.0100   -0.0002
   860        0.6388             nan     0.0100   -0.0001
   880        0.6363             nan     0.0100   -0.0002
   900        0.6337             nan     0.0100   -0.0001
   920        0.6306             nan     0.0100   -0.0001
   940        0.6285             nan     0.0100   -0.0001
   960        0.6260             nan     0.0100   -0.0002
   980        0.6237             nan     0.0100   -0.0002
  1000        0.6214             nan     0.0100   -0.0002
  1020        0.6192             nan     0.0100   -0.0001
  1040        0.6169             nan     0.0100   -0.0002
  1060        0.6143             nan     0.0100   -0.0001
  1080        0.6122             nan     0.0100   -0.0002
  1100        0.6098             nan     0.0100   -0.0002

- Fold04.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2742             nan     0.1000    0.0296
     2        1.2307             nan     0.1000    0.0240
     3        1.1896             nan     0.1000    0.0208
     4        1.1573             nan     0.1000    0.0165
     5        1.1289             nan     0.1000    0.0130
     6        1.1013             nan     0.1000    0.0117
     7        1.0816             nan     0.1000    0.0088
     8        1.0652             nan     0.1000    0.0082
     9        1.0475             nan     0.1000    0.0084
    10        1.0326             nan     0.1000    0.0051
    20        0.9352             nan     0.1000    0.0028
    40        0.8505             nan     0.1000    0.0009
    60        0.8139             nan     0.1000   -0.0005
    80        0.7862             nan     0.1000   -0.0002
   100        0.7691             nan     0.1000   -0.0002
   120        0.7561             nan     0.1000   -0.0005
   140        0.7459             nan     0.1000   -0.0001
   160        0.7363             nan     0.1000   -0.0006
   180        0.7283             nan     0.1000   -0.0016
   200        0.7210             nan     0.1000   -0.0006
   220        0.7138             nan     0.1000   -0.0005
   240        0.7082             nan     0.1000   -0.0016
   260        0.7016             nan     0.1000   -0.0017
   280        0.6986             nan     0.1000   -0.0015
   300        0.6937             nan     0.1000   -0.0010
   320        0.6906             nan     0.1000   -0.0009
   340        0.6860             nan     0.1000   -0.0007
   360        0.6816             nan     0.1000   -0.0008
   380        0.6788             nan     0.1000   -0.0008
   400        0.6753             nan     0.1000   -0.0002
   420        0.6724             nan     0.1000   -0.0007
   440        0.6692             nan     0.1000   -0.0006
   460        0.6671             nan     0.1000   -0.0026
   480        0.6654             nan     0.1000   -0.0003
   500        0.6634             nan     0.1000   -0.0003
   520        0.6602             nan     0.1000   -0.0009
   540        0.6579             nan     0.1000   -0.0007
   560        0.6564             nan     0.1000   -0.0010
   580        0.6552             nan     0.1000   -0.0006
   600        0.6530             nan     0.1000   -0.0009
   620        0.6511             nan     0.1000   -0.0004
   640        0.6499             nan     0.1000   -0.0006
   660        0.6493             nan     0.1000   -0.0013
   680        0.6463             nan     0.1000   -0.0003
   700        0.6446             nan     0.1000   -0.0004
   720        0.6421             nan     0.1000   -0.0008
   740        0.6416             nan     0.1000   -0.0005
   760        0.6386             nan     0.1000   -0.0015
   780        0.6372             nan     0.1000   -0.0010
   800        0.6345             nan     0.1000   -0.0003
   820        0.6329             nan     0.1000   -0.0011
   840        0.6310             nan     0.1000   -0.0008
   860        0.6291             nan     0.1000   -0.0013
   880        0.6280             nan     0.1000   -0.0006
   900        0.6260             nan     0.1000   -0.0013
   920        0.6254             nan     0.1000   -0.0006
   940        0.6224             nan     0.1000   -0.0007
   960        0.6205             nan     0.1000   -0.0005
   980        0.6185             nan     0.1000   -0.0002
  1000        0.6170             nan     0.1000   -0.0004
  1020        0.6162             nan     0.1000   -0.0006
  1040        0.6146             nan     0.1000   -0.0009
  1060        0.6132             nan     0.1000   -0.0007
  1080        0.6125             nan     0.1000   -0.0008
  1100        0.6102             nan     0.1000   -0.0004

- Fold04.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2528             nan     0.1000    0.0368
     2        1.1946             nan     0.1000    0.0300
     3        1.1427             nan     0.1000    0.0250
     4        1.0969             nan     0.1000    0.0209
     5        1.0613             nan     0.1000    0.0163
     6        1.0319             nan     0.1000    0.0145
     7        1.0051             nan     0.1000    0.0131
     8        0.9811             nan     0.1000    0.0097
     9        0.9633             nan     0.1000    0.0085
    10        0.9462             nan     0.1000    0.0086
    20        0.8440             nan     0.1000    0.0010
    40        0.7617             nan     0.1000    0.0001
    60        0.7217             nan     0.1000   -0.0002
    80        0.6960             nan     0.1000    0.0002
   100        0.6757             nan     0.1000   -0.0009
   120        0.6596             nan     0.1000   -0.0012
   140        0.6423             nan     0.1000   -0.0007
   160        0.6298             nan     0.1000   -0.0025
   180        0.6184             nan     0.1000   -0.0002
   200        0.6079             nan     0.1000   -0.0009
   220        0.6002             nan     0.1000   -0.0011
   240        0.5888             nan     0.1000   -0.0005
   260        0.5792             nan     0.1000   -0.0006
   280        0.5701             nan     0.1000   -0.0014
   300        0.5632             nan     0.1000   -0.0017
   320        0.5560             nan     0.1000   -0.0020
   340        0.5500             nan     0.1000   -0.0010
   360        0.5434             nan     0.1000   -0.0020
   380        0.5363             nan     0.1000   -0.0008
   400        0.5286             nan     0.1000   -0.0022
   420        0.5228             nan     0.1000   -0.0004
   440        0.5148             nan     0.1000   -0.0010
   460        0.5066             nan     0.1000   -0.0010
   480        0.5000             nan     0.1000   -0.0006
   500        0.4916             nan     0.1000   -0.0003
   520        0.4859             nan     0.1000   -0.0006
   540        0.4823             nan     0.1000   -0.0010
   560        0.4767             nan     0.1000   -0.0002
   580        0.4717             nan     0.1000   -0.0005
   600        0.4672             nan     0.1000   -0.0005
   620        0.4623             nan     0.1000   -0.0007
   640        0.4574             nan     0.1000   -0.0016
   660        0.4516             nan     0.1000   -0.0006
   680        0.4483             nan     0.1000   -0.0010
   700        0.4424             nan     0.1000   -0.0009
   720        0.4399             nan     0.1000   -0.0013
   740        0.4341             nan     0.1000   -0.0007
   760        0.4300             nan     0.1000   -0.0008
   780        0.4257             nan     0.1000   -0.0004
   800        0.4228             nan     0.1000   -0.0009
   820        0.4186             nan     0.1000   -0.0008
   840        0.4153             nan     0.1000   -0.0010
   860        0.4125             nan     0.1000   -0.0017
   880        0.4094             nan     0.1000   -0.0008
   900        0.4057             nan     0.1000   -0.0012
   920        0.4021             nan     0.1000   -0.0006
   940        0.3971             nan     0.1000   -0.0005
   960        0.3946             nan     0.1000   -0.0014
   980        0.3916             nan     0.1000   -0.0010
  1000        0.3883             nan     0.1000   -0.0007
  1020        0.3841             nan     0.1000   -0.0014
  1040        0.3817             nan     0.1000   -0.0012
  1060        0.3798             nan     0.1000   -0.0009
  1080        0.3764             nan     0.1000   -0.0009
  1100        0.3738             nan     0.1000   -0.0009

- Fold04.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2522             nan     0.1000    0.0420
     2        1.1870             nan     0.1000    0.0332
     3        1.1287             nan     0.1000    0.0275
     4        1.0794             nan     0.1000    0.0230
     5        1.0413             nan     0.1000    0.0185
     6        1.0112             nan     0.1000    0.0120
     7        0.9824             nan     0.1000    0.0149
     8        0.9562             nan     0.1000    0.0111
     9        0.9407             nan     0.1000    0.0065
    10        0.9194             nan     0.1000    0.0086
    20        0.8088             nan     0.1000    0.0020
    40        0.7289             nan     0.1000    0.0003
    60        0.6888             nan     0.1000   -0.0002
    80        0.6569             nan     0.1000   -0.0006
   100        0.6276             nan     0.1000   -0.0017
   120        0.6024             nan     0.1000   -0.0007
   140        0.5839             nan     0.1000   -0.0006
   160        0.5637             nan     0.1000   -0.0008
   180        0.5492             nan     0.1000   -0.0011
   200        0.5340             nan     0.1000   -0.0008
   220        0.5220             nan     0.1000   -0.0008
   240        0.5075             nan     0.1000   -0.0008
   260        0.4951             nan     0.1000   -0.0004
   280        0.4819             nan     0.1000   -0.0012
   300        0.4682             nan     0.1000   -0.0011
   320        0.4566             nan     0.1000   -0.0009
   340        0.4456             nan     0.1000   -0.0010
   360        0.4374             nan     0.1000   -0.0014
   380        0.4267             nan     0.1000   -0.0004
   400        0.4161             nan     0.1000   -0.0016
   420        0.4071             nan     0.1000   -0.0014
   440        0.3975             nan     0.1000   -0.0006
   460        0.3909             nan     0.1000   -0.0008
   480        0.3836             nan     0.1000   -0.0012
   500        0.3782             nan     0.1000   -0.0010
   520        0.3728             nan     0.1000   -0.0015
   540        0.3670             nan     0.1000   -0.0011
   560        0.3595             nan     0.1000   -0.0008
   580        0.3510             nan     0.1000   -0.0016
   600        0.3444             nan     0.1000   -0.0008
   620        0.3369             nan     0.1000   -0.0012
   640        0.3310             nan     0.1000   -0.0008
   660        0.3257             nan     0.1000   -0.0011
   680        0.3202             nan     0.1000   -0.0012
   700        0.3120             nan     0.1000   -0.0007
   720        0.3071             nan     0.1000   -0.0005
   740        0.3008             nan     0.1000   -0.0005
   760        0.2963             nan     0.1000   -0.0005
   780        0.2921             nan     0.1000   -0.0013
   800        0.2866             nan     0.1000   -0.0009
   820        0.2834             nan     0.1000   -0.0010
   840        0.2787             nan     0.1000   -0.0012
   860        0.2740             nan     0.1000   -0.0014
   880        0.2696             nan     0.1000   -0.0005
   900        0.2658             nan     0.1000   -0.0005
   920        0.2617             nan     0.1000   -0.0010
   940        0.2574             nan     0.1000   -0.0009
   960        0.2531             nan     0.1000   -0.0005
   980        0.2485             nan     0.1000   -0.0007
  1000        0.2462             nan     0.1000   -0.0003
  1020        0.2429             nan     0.1000   -0.0007
  1040        0.2397             nan     0.1000   -0.0008
  1060        0.2389             nan     0.1000   -0.0012
  1080        0.2347             nan     0.1000   -0.0007
  1100        0.2324             nan     0.1000   -0.0009

- Fold04.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3260             nan     0.0100    0.0030
     2        1.3197             nan     0.0100    0.0029
     3        1.3139             nan     0.0100    0.0029
     4        1.3079             nan     0.0100    0.0028
     5        1.3022             nan     0.0100    0.0028
     6        1.2968             nan     0.0100    0.0028
     7        1.2916             nan     0.0100    0.0026
     8        1.2867             nan     0.0100    0.0026
     9        1.2823             nan     0.0100    0.0025
    10        1.2769             nan     0.0100    0.0025
    20        1.2306             nan     0.0100    0.0021
    40        1.1615             nan     0.0100    0.0014
    60        1.1122             nan     0.0100    0.0011
    80        1.0726             nan     0.0100    0.0008
   100        1.0404             nan     0.0100    0.0006
   120        1.0160             nan     0.0100    0.0005
   140        0.9943             nan     0.0100    0.0003
   160        0.9756             nan     0.0100    0.0003
   180        0.9598             nan     0.0100    0.0003
   200        0.9460             nan     0.0100    0.0003
   220        0.9337             nan     0.0100    0.0001
   240        0.9228             nan     0.0100    0.0001
   260        0.9129             nan     0.0100    0.0002
   280        0.9040             nan     0.0100    0.0001
   300        0.8967             nan     0.0100    0.0001
   320        0.8895             nan     0.0100    0.0002
   340        0.8827             nan     0.0100    0.0001
   360        0.8767             nan     0.0100    0.0000
   380        0.8709             nan     0.0100    0.0000
   400        0.8662             nan     0.0100    0.0001
   420        0.8616             nan     0.0100    0.0001
   440        0.8569             nan     0.0100    0.0001
   460        0.8524             nan     0.0100    0.0001
   480        0.8483             nan     0.0100    0.0000
   500        0.8446             nan     0.0100   -0.0001
   520        0.8408             nan     0.0100    0.0001
   540        0.8371             nan     0.0100    0.0000
   560        0.8336             nan     0.0100   -0.0000
   580        0.8304             nan     0.0100   -0.0000
   600        0.8273             nan     0.0100    0.0000
   620        0.8247             nan     0.0100   -0.0001
   640        0.8221             nan     0.0100   -0.0001
   660        0.8195             nan     0.0100    0.0000
   680        0.8169             nan     0.0100   -0.0000
   700        0.8144             nan     0.0100   -0.0000
   720        0.8120             nan     0.0100   -0.0000
   740        0.8098             nan     0.0100   -0.0001
   760        0.8077             nan     0.0100    0.0000
   780        0.8055             nan     0.0100    0.0000
   800        0.8037             nan     0.0100   -0.0000
   820        0.8017             nan     0.0100    0.0000
   840        0.7999             nan     0.0100   -0.0001
   860        0.7981             nan     0.0100   -0.0000
   880        0.7962             nan     0.0100    0.0000
   900        0.7944             nan     0.0100   -0.0000
   920        0.7926             nan     0.0100   -0.0000
   940        0.7910             nan     0.0100   -0.0000
   960        0.7893             nan     0.0100   -0.0000
   980        0.7877             nan     0.0100   -0.0000
  1000        0.7861             nan     0.0100   -0.0001
  1020        0.7846             nan     0.0100   -0.0000
  1040        0.7832             nan     0.0100   -0.0002
  1060        0.7818             nan     0.0100   -0.0000
  1080        0.7806             nan     0.0100   -0.0000
  1100        0.7793             nan     0.0100   -0.0001

- Fold05.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3233             nan     0.0100    0.0039
     2        1.3162             nan     0.0100    0.0036
     3        1.3087             nan     0.0100    0.0035
     4        1.3015             nan     0.0100    0.0036
     5        1.2945             nan     0.0100    0.0035
     6        1.2874             nan     0.0100    0.0033
     7        1.2805             nan     0.0100    0.0036
     8        1.2737             nan     0.0100    0.0032
     9        1.2667             nan     0.0100    0.0033
    10        1.2602             nan     0.0100    0.0032
    20        1.2036             nan     0.0100    0.0026
    40        1.1156             nan     0.0100    0.0016
    60        1.0499             nan     0.0100    0.0014
    80        0.9996             nan     0.0100    0.0009
   100        0.9604             nan     0.0100    0.0008
   120        0.9317             nan     0.0100    0.0006
   140        0.9088             nan     0.0100    0.0003
   160        0.8911             nan     0.0100    0.0004
   180        0.8749             nan     0.0100    0.0003
   200        0.8621             nan     0.0100    0.0001
   220        0.8502             nan     0.0100    0.0001
   240        0.8400             nan     0.0100    0.0001
   260        0.8306             nan     0.0100    0.0001
   280        0.8226             nan     0.0100    0.0000
   300        0.8152             nan     0.0100    0.0001
   320        0.8083             nan     0.0100    0.0001
   340        0.8024             nan     0.0100    0.0000
   360        0.7965             nan     0.0100    0.0000
   380        0.7911             nan     0.0100    0.0001
   400        0.7861             nan     0.0100   -0.0000
   420        0.7816             nan     0.0100   -0.0000
   440        0.7771             nan     0.0100   -0.0000
   460        0.7730             nan     0.0100   -0.0000
   480        0.7687             nan     0.0100    0.0000
   500        0.7644             nan     0.0100   -0.0001
   520        0.7611             nan     0.0100   -0.0001
   540        0.7576             nan     0.0100    0.0000
   560        0.7544             nan     0.0100   -0.0000
   580        0.7510             nan     0.0100   -0.0001
   600        0.7477             nan     0.0100    0.0000
   620        0.7446             nan     0.0100    0.0000
   640        0.7414             nan     0.0100   -0.0000
   660        0.7383             nan     0.0100   -0.0000
   680        0.7357             nan     0.0100   -0.0001
   700        0.7327             nan     0.0100   -0.0000
   720        0.7298             nan     0.0100   -0.0000
   740        0.7274             nan     0.0100   -0.0001
   760        0.7247             nan     0.0100   -0.0001
   780        0.7218             nan     0.0100   -0.0001
   800        0.7194             nan     0.0100   -0.0001
   820        0.7173             nan     0.0100   -0.0000
   840        0.7152             nan     0.0100   -0.0001
   860        0.7130             nan     0.0100   -0.0001
   880        0.7108             nan     0.0100   -0.0001
   900        0.7083             nan     0.0100   -0.0001
   920        0.7059             nan     0.0100   -0.0000
   940        0.7040             nan     0.0100   -0.0001
   960        0.7016             nan     0.0100   -0.0001
   980        0.6996             nan     0.0100   -0.0001
  1000        0.6976             nan     0.0100   -0.0001
  1020        0.6955             nan     0.0100    0.0000
  1040        0.6936             nan     0.0100   -0.0001
  1060        0.6919             nan     0.0100   -0.0001
  1080        0.6899             nan     0.0100   -0.0001
  1100        0.6881             nan     0.0100   -0.0001

- Fold05.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3233             nan     0.0100    0.0040
     2        1.3154             nan     0.0100    0.0040
     3        1.3075             nan     0.0100    0.0037
     4        1.2994             nan     0.0100    0.0037
     5        1.2914             nan     0.0100    0.0037
     6        1.2834             nan     0.0100    0.0035
     7        1.2761             nan     0.0100    0.0036
     8        1.2689             nan     0.0100    0.0036
     9        1.2617             nan     0.0100    0.0033
    10        1.2551             nan     0.0100    0.0034
    20        1.1921             nan     0.0100    0.0029
    40        1.0929             nan     0.0100    0.0020
    60        1.0210             nan     0.0100    0.0016
    80        0.9681             nan     0.0100    0.0011
   100        0.9285             nan     0.0100    0.0007
   120        0.8991             nan     0.0100    0.0007
   140        0.8751             nan     0.0100    0.0005
   160        0.8564             nan     0.0100    0.0003
   180        0.8400             nan     0.0100   -0.0000
   200        0.8262             nan     0.0100    0.0001
   220        0.8134             nan     0.0100    0.0001
   240        0.8025             nan     0.0100    0.0001
   260        0.7928             nan     0.0100   -0.0000
   280        0.7841             nan     0.0100    0.0001
   300        0.7763             nan     0.0100   -0.0000
   320        0.7695             nan     0.0100    0.0001
   340        0.7625             nan     0.0100   -0.0000
   360        0.7561             nan     0.0100   -0.0000
   380        0.7499             nan     0.0100    0.0001
   400        0.7445             nan     0.0100   -0.0002
   420        0.7388             nan     0.0100    0.0001
   440        0.7330             nan     0.0100   -0.0000
   460        0.7279             nan     0.0100    0.0000
   480        0.7232             nan     0.0100   -0.0001
   500        0.7186             nan     0.0100   -0.0001
   520        0.7149             nan     0.0100   -0.0001
   540        0.7107             nan     0.0100   -0.0001
   560        0.7071             nan     0.0100   -0.0002
   580        0.7031             nan     0.0100   -0.0000
   600        0.6990             nan     0.0100   -0.0001
   620        0.6953             nan     0.0100    0.0001
   640        0.6916             nan     0.0100   -0.0001
   660        0.6885             nan     0.0100   -0.0002
   680        0.6848             nan     0.0100   -0.0001
   700        0.6811             nan     0.0100   -0.0001
   720        0.6779             nan     0.0100   -0.0001
   740        0.6749             nan     0.0100   -0.0001
   760        0.6713             nan     0.0100   -0.0001
   780        0.6683             nan     0.0100   -0.0000
   800        0.6649             nan     0.0100   -0.0001
   820        0.6616             nan     0.0100   -0.0001
   840        0.6585             nan     0.0100   -0.0002
   860        0.6557             nan     0.0100   -0.0001
   880        0.6532             nan     0.0100   -0.0001
   900        0.6504             nan     0.0100   -0.0000
   920        0.6479             nan     0.0100    0.0000
   940        0.6451             nan     0.0100   -0.0000
   960        0.6421             nan     0.0100   -0.0001
   980        0.6396             nan     0.0100   -0.0001
  1000        0.6375             nan     0.0100   -0.0002
  1020        0.6351             nan     0.0100   -0.0001
  1040        0.6325             nan     0.0100   -0.0001
  1060        0.6302             nan     0.0100   -0.0002
  1080        0.6279             nan     0.0100   -0.0000
  1100        0.6258             nan     0.0100   -0.0000

- Fold05.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2744             nan     0.1000    0.0289
     2        1.2247             nan     0.1000    0.0230
     3        1.1912             nan     0.1000    0.0189
     4        1.1563             nan     0.1000    0.0141
     5        1.1278             nan     0.1000    0.0138
     6        1.1066             nan     0.1000    0.0083
     7        1.0835             nan     0.1000    0.0108
     8        1.0646             nan     0.1000    0.0085
     9        1.0468             nan     0.1000    0.0068
    10        1.0339             nan     0.1000    0.0059
    20        0.9420             nan     0.1000    0.0023
    40        0.8660             nan     0.1000    0.0008
    60        0.8267             nan     0.1000   -0.0008
    80        0.8019             nan     0.1000   -0.0004
   100        0.7856             nan     0.1000   -0.0002
   120        0.7711             nan     0.1000   -0.0002
   140        0.7630             nan     0.1000   -0.0001
   160        0.7552             nan     0.1000   -0.0005
   180        0.7457             nan     0.1000   -0.0006
   200        0.7385             nan     0.1000   -0.0002
   220        0.7330             nan     0.1000   -0.0006
   240        0.7273             nan     0.1000   -0.0001
   260        0.7215             nan     0.1000   -0.0005
   280        0.7172             nan     0.1000   -0.0001
   300        0.7134             nan     0.1000   -0.0012
   320        0.7088             nan     0.1000   -0.0007
   340        0.7055             nan     0.1000   -0.0005
   360        0.7011             nan     0.1000   -0.0010
   380        0.6973             nan     0.1000   -0.0006
   400        0.6950             nan     0.1000   -0.0007
   420        0.6921             nan     0.1000   -0.0006
   440        0.6896             nan     0.1000   -0.0013
   460        0.6862             nan     0.1000   -0.0013
   480        0.6846             nan     0.1000   -0.0010
   500        0.6826             nan     0.1000   -0.0006
   520        0.6802             nan     0.1000   -0.0010
   540        0.6773             nan     0.1000   -0.0005
   560        0.6761             nan     0.1000   -0.0006
   580        0.6735             nan     0.1000   -0.0006
   600        0.6725             nan     0.1000   -0.0014
   620        0.6688             nan     0.1000   -0.0007
   640        0.6675             nan     0.1000   -0.0012
   660        0.6658             nan     0.1000   -0.0021
   680        0.6629             nan     0.1000   -0.0004
   700        0.6619             nan     0.1000   -0.0003
   720        0.6590             nan     0.1000   -0.0006
   740        0.6567             nan     0.1000   -0.0002
   760        0.6549             nan     0.1000   -0.0003
   780        0.6529             nan     0.1000   -0.0002
   800        0.6517             nan     0.1000   -0.0011
   820        0.6502             nan     0.1000   -0.0011
   840        0.6471             nan     0.1000   -0.0010
   860        0.6449             nan     0.1000   -0.0008
   880        0.6436             nan     0.1000   -0.0009
   900        0.6421             nan     0.1000   -0.0008
   920        0.6409             nan     0.1000   -0.0013
   940        0.6395             nan     0.1000   -0.0004
   960        0.6368             nan     0.1000   -0.0007
   980        0.6356             nan     0.1000   -0.0004
  1000        0.6355             nan     0.1000   -0.0014
  1020        0.6340             nan     0.1000   -0.0009
  1040        0.6327             nan     0.1000   -0.0006
  1060        0.6307             nan     0.1000   -0.0004
  1080        0.6295             nan     0.1000   -0.0005
  1100        0.6280             nan     0.1000   -0.0003

- Fold05.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2580             nan     0.1000    0.0336
     2        1.1994             nan     0.1000    0.0303
     3        1.1545             nan     0.1000    0.0242
     4        1.1098             nan     0.1000    0.0209
     5        1.0758             nan     0.1000    0.0153
     6        1.0463             nan     0.1000    0.0129
     7        1.0220             nan     0.1000    0.0121
     8        1.0014             nan     0.1000    0.0106
     9        0.9790             nan     0.1000    0.0096
    10        0.9633             nan     0.1000    0.0087
    20        0.8615             nan     0.1000    0.0019
    40        0.7876             nan     0.1000    0.0004
    60        0.7537             nan     0.1000   -0.0004
    80        0.7249             nan     0.1000   -0.0012
   100        0.7044             nan     0.1000   -0.0011
   120        0.6842             nan     0.1000   -0.0012
   140        0.6679             nan     0.1000   -0.0003
   160        0.6527             nan     0.1000   -0.0009
   180        0.6396             nan     0.1000   -0.0009
   200        0.6295             nan     0.1000   -0.0007
   220        0.6176             nan     0.1000    0.0003
   240        0.6044             nan     0.1000   -0.0015
   260        0.5957             nan     0.1000   -0.0009
   280        0.5892             nan     0.1000   -0.0011
   300        0.5795             nan     0.1000   -0.0012
   320        0.5721             nan     0.1000   -0.0007
   340        0.5627             nan     0.1000   -0.0012
   360        0.5565             nan     0.1000   -0.0009
   380        0.5458             nan     0.1000   -0.0013
   400        0.5398             nan     0.1000   -0.0007
   420        0.5320             nan     0.1000   -0.0013
   440        0.5248             nan     0.1000   -0.0008
   460        0.5175             nan     0.1000   -0.0008
   480        0.5121             nan     0.1000   -0.0008
   500        0.5046             nan     0.1000   -0.0005
   520        0.5000             nan     0.1000   -0.0011
   540        0.4944             nan     0.1000   -0.0014
   560        0.4905             nan     0.1000   -0.0014
   580        0.4839             nan     0.1000   -0.0009
   600        0.4783             nan     0.1000   -0.0011
   620        0.4734             nan     0.1000   -0.0019
   640        0.4679             nan     0.1000   -0.0012
   660        0.4630             nan     0.1000   -0.0007
   680        0.4582             nan     0.1000   -0.0007
   700        0.4516             nan     0.1000   -0.0003
   720        0.4468             nan     0.1000   -0.0010
   740        0.4421             nan     0.1000   -0.0005
   760        0.4374             nan     0.1000   -0.0002
   780        0.4336             nan     0.1000   -0.0006
   800        0.4310             nan     0.1000   -0.0017
   820        0.4292             nan     0.1000   -0.0009
   840        0.4258             nan     0.1000   -0.0006
   860        0.4229             nan     0.1000   -0.0017
   880        0.4204             nan     0.1000   -0.0012
   900        0.4165             nan     0.1000   -0.0012
   920        0.4117             nan     0.1000   -0.0001
   940        0.4104             nan     0.1000   -0.0006
   960        0.4068             nan     0.1000   -0.0010
   980        0.4047             nan     0.1000   -0.0011
  1000        0.3999             nan     0.1000   -0.0006
  1020        0.3969             nan     0.1000   -0.0011
  1040        0.3933             nan     0.1000   -0.0009
  1060        0.3909             nan     0.1000   -0.0010
  1080        0.3884             nan     0.1000   -0.0009
  1100        0.3853             nan     0.1000   -0.0008

- Fold05.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2505             nan     0.1000    0.0366
     2        1.1835             nan     0.1000    0.0332
     3        1.1309             nan     0.1000    0.0271
     4        1.0855             nan     0.1000    0.0210
     5        1.0453             nan     0.1000    0.0184
     6        1.0119             nan     0.1000    0.0169
     7        0.9829             nan     0.1000    0.0126
     8        0.9557             nan     0.1000    0.0126
     9        0.9340             nan     0.1000    0.0090
    10        0.9185             nan     0.1000    0.0072
    20        0.8228             nan     0.1000    0.0012
    40        0.7523             nan     0.1000   -0.0006
    60        0.7112             nan     0.1000   -0.0021
    80        0.6750             nan     0.1000   -0.0009
   100        0.6463             nan     0.1000   -0.0010
   120        0.6243             nan     0.1000   -0.0007
   140        0.5989             nan     0.1000   -0.0010
   160        0.5798             nan     0.1000   -0.0011
   180        0.5665             nan     0.1000   -0.0004
   200        0.5481             nan     0.1000   -0.0006
   220        0.5358             nan     0.1000   -0.0010
   240        0.5240             nan     0.1000   -0.0011
   260        0.5123             nan     0.1000   -0.0015
   280        0.4993             nan     0.1000   -0.0008
   300        0.4889             nan     0.1000   -0.0010
   320        0.4800             nan     0.1000   -0.0017
   340        0.4720             nan     0.1000   -0.0015
   360        0.4621             nan     0.1000   -0.0005
   380        0.4534             nan     0.1000   -0.0015
   400        0.4463             nan     0.1000   -0.0011
   420        0.4362             nan     0.1000   -0.0012
   440        0.4273             nan     0.1000   -0.0010
   460        0.4179             nan     0.1000   -0.0014
   480        0.4105             nan     0.1000   -0.0007
   500        0.4056             nan     0.1000   -0.0009
   520        0.3978             nan     0.1000   -0.0014
   540        0.3912             nan     0.1000   -0.0013
   560        0.3834             nan     0.1000   -0.0014
   580        0.3774             nan     0.1000   -0.0008
   600        0.3707             nan     0.1000   -0.0014
   620        0.3633             nan     0.1000   -0.0011
   640        0.3591             nan     0.1000   -0.0012
   660        0.3526             nan     0.1000   -0.0012
   680        0.3482             nan     0.1000   -0.0012
   700        0.3430             nan     0.1000   -0.0009
   720        0.3385             nan     0.1000   -0.0011
   740        0.3318             nan     0.1000   -0.0018
   760        0.3268             nan     0.1000   -0.0010
   780        0.3211             nan     0.1000   -0.0013
   800        0.3165             nan     0.1000   -0.0009
   820        0.3134             nan     0.1000   -0.0005
   840        0.3095             nan     0.1000   -0.0010
   860        0.3047             nan     0.1000   -0.0010
   880        0.2997             nan     0.1000   -0.0013
   900        0.2959             nan     0.1000   -0.0006
   920        0.2921             nan     0.1000   -0.0005
   940        0.2904             nan     0.1000   -0.0010
   960        0.2875             nan     0.1000   -0.0007
   980        0.2840             nan     0.1000   -0.0008
  1000        0.2797             nan     0.1000   -0.0005
  1020        0.2770             nan     0.1000   -0.0010
  1040        0.2757             nan     0.1000   -0.0006
  1060        0.2712             nan     0.1000   -0.0008
  1080        0.2676             nan     0.1000   -0.0011
  1100        0.2637             nan     0.1000   -0.0006

- Fold05.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3264             nan     0.0100    0.0028
     2        1.3205             nan     0.0100    0.0028
     3        1.3153             nan     0.0100    0.0027
     4        1.3103             nan     0.0100    0.0027
     5        1.3055             nan     0.0100    0.0026
     6        1.3001             nan     0.0100    0.0026
     7        1.2943             nan     0.0100    0.0025
     8        1.2891             nan     0.0100    0.0025
     9        1.2843             nan     0.0100    0.0024
    10        1.2791             nan     0.0100    0.0023
    20        1.2344             nan     0.0100    0.0019
    40        1.1701             nan     0.0100    0.0014
    60        1.1240             nan     0.0100    0.0010
    80        1.0880             nan     0.0100    0.0006
   100        1.0589             nan     0.0100    0.0006
   120        1.0340             nan     0.0100    0.0003
   140        1.0138             nan     0.0100    0.0004
   160        0.9966             nan     0.0100    0.0003
   180        0.9821             nan     0.0100    0.0003
   200        0.9692             nan     0.0100    0.0002
   220        0.9578             nan     0.0100    0.0002
   240        0.9480             nan     0.0100    0.0002
   260        0.9390             nan     0.0100    0.0001
   280        0.9303             nan     0.0100    0.0002
   300        0.9220             nan     0.0100    0.0002
   320        0.9151             nan     0.0100    0.0001
   340        0.9085             nan     0.0100    0.0000
   360        0.9021             nan     0.0100    0.0001
   380        0.8967             nan     0.0100    0.0001
   400        0.8912             nan     0.0100    0.0001
   420        0.8865             nan     0.0100    0.0001
   440        0.8817             nan     0.0100   -0.0000
   460        0.8774             nan     0.0100    0.0000
   480        0.8731             nan     0.0100    0.0000
   500        0.8695             nan     0.0100    0.0000
   520        0.8655             nan     0.0100    0.0000
   540        0.8616             nan     0.0100    0.0000
   560        0.8579             nan     0.0100   -0.0000
   580        0.8546             nan     0.0100    0.0000
   600        0.8516             nan     0.0100   -0.0000
   620        0.8490             nan     0.0100   -0.0000
   640        0.8461             nan     0.0100   -0.0000
   660        0.8432             nan     0.0100   -0.0000
   680        0.8406             nan     0.0100   -0.0000
   700        0.8378             nan     0.0100   -0.0000
   720        0.8354             nan     0.0100    0.0000
   740        0.8330             nan     0.0100    0.0000
   760        0.8306             nan     0.0100   -0.0000
   780        0.8283             nan     0.0100   -0.0000
   800        0.8263             nan     0.0100   -0.0000
   820        0.8243             nan     0.0100    0.0000
   840        0.8223             nan     0.0100   -0.0000
   860        0.8205             nan     0.0100   -0.0000
   880        0.8186             nan     0.0100   -0.0000
   900        0.8168             nan     0.0100   -0.0001
   920        0.8152             nan     0.0100   -0.0000
   940        0.8135             nan     0.0100   -0.0001
   960        0.8119             nan     0.0100   -0.0000
   980        0.8103             nan     0.0100   -0.0001
  1000        0.8087             nan     0.0100   -0.0001
  1020        0.8074             nan     0.0100   -0.0000
  1040        0.8063             nan     0.0100   -0.0001
  1060        0.8050             nan     0.0100   -0.0001
  1080        0.8037             nan     0.0100   -0.0001
  1100        0.8023             nan     0.0100   -0.0000

- Fold06.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3250             nan     0.0100    0.0035
     2        1.3174             nan     0.0100    0.0034
     3        1.3102             nan     0.0100    0.0034
     4        1.3038             nan     0.0100    0.0033
     5        1.2975             nan     0.0100    0.0033
     6        1.2910             nan     0.0100    0.0032
     7        1.2843             nan     0.0100    0.0031
     8        1.2779             nan     0.0100    0.0030
     9        1.2715             nan     0.0100    0.0027
    10        1.2656             nan     0.0100    0.0030
    20        1.2118             nan     0.0100    0.0025
    40        1.1272             nan     0.0100    0.0016
    60        1.0650             nan     0.0100    0.0013
    80        1.0183             nan     0.0100    0.0009
   100        0.9835             nan     0.0100    0.0007
   120        0.9563             nan     0.0100    0.0005
   140        0.9341             nan     0.0100    0.0003
   160        0.9164             nan     0.0100    0.0004
   180        0.9003             nan     0.0100    0.0004
   200        0.8870             nan     0.0100    0.0002
   220        0.8749             nan     0.0100    0.0001
   240        0.8643             nan     0.0100    0.0001
   260        0.8553             nan     0.0100    0.0000
   280        0.8470             nan     0.0100    0.0000
   300        0.8393             nan     0.0100    0.0001
   320        0.8319             nan     0.0100    0.0000
   340        0.8256             nan     0.0100   -0.0000
   360        0.8193             nan     0.0100    0.0001
   380        0.8133             nan     0.0100    0.0001
   400        0.8086             nan     0.0100    0.0001
   420        0.8037             nan     0.0100   -0.0000
   440        0.7993             nan     0.0100   -0.0000
   460        0.7947             nan     0.0100   -0.0001
   480        0.7911             nan     0.0100   -0.0000
   500        0.7872             nan     0.0100   -0.0001
   520        0.7833             nan     0.0100    0.0000
   540        0.7798             nan     0.0100   -0.0001
   560        0.7767             nan     0.0100   -0.0001
   580        0.7736             nan     0.0100   -0.0000
   600        0.7702             nan     0.0100   -0.0001
   620        0.7669             nan     0.0100   -0.0000
   640        0.7641             nan     0.0100   -0.0001
   660        0.7611             nan     0.0100   -0.0001
   680        0.7584             nan     0.0100   -0.0000
   700        0.7553             nan     0.0100   -0.0001
   720        0.7528             nan     0.0100   -0.0001
   740        0.7507             nan     0.0100   -0.0001
   760        0.7484             nan     0.0100   -0.0000
   780        0.7462             nan     0.0100   -0.0001
   800        0.7438             nan     0.0100   -0.0001
   820        0.7413             nan     0.0100   -0.0000
   840        0.7395             nan     0.0100   -0.0001
   860        0.7372             nan     0.0100   -0.0001
   880        0.7350             nan     0.0100   -0.0002
   900        0.7327             nan     0.0100   -0.0000
   920        0.7302             nan     0.0100   -0.0000
   940        0.7284             nan     0.0100   -0.0001
   960        0.7261             nan     0.0100   -0.0001
   980        0.7244             nan     0.0100   -0.0002
  1000        0.7224             nan     0.0100    0.0000
  1020        0.7205             nan     0.0100   -0.0000
  1040        0.7185             nan     0.0100   -0.0002
  1060        0.7165             nan     0.0100   -0.0000
  1080        0.7148             nan     0.0100   -0.0001
  1100        0.7129             nan     0.0100   -0.0001

- Fold06.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3239             nan     0.0100    0.0039
     2        1.3162             nan     0.0100    0.0037
     3        1.3081             nan     0.0100    0.0037
     4        1.3007             nan     0.0100    0.0035
     5        1.2935             nan     0.0100    0.0034
     6        1.2867             nan     0.0100    0.0035
     7        1.2796             nan     0.0100    0.0031
     8        1.2726             nan     0.0100    0.0035
     9        1.2660             nan     0.0100    0.0031
    10        1.2594             nan     0.0100    0.0031
    20        1.2001             nan     0.0100    0.0027
    40        1.1066             nan     0.0100    0.0018
    60        1.0379             nan     0.0100    0.0013
    80        0.9873             nan     0.0100    0.0010
   100        0.9514             nan     0.0100    0.0003
   120        0.9220             nan     0.0100    0.0004
   140        0.8989             nan     0.0100    0.0003
   160        0.8780             nan     0.0100    0.0002
   180        0.8614             nan     0.0100    0.0002
   200        0.8472             nan     0.0100    0.0001
   220        0.8358             nan     0.0100    0.0001
   240        0.8235             nan     0.0100    0.0001
   260        0.8138             nan     0.0100    0.0001
   280        0.8050             nan     0.0100    0.0001
   300        0.7967             nan     0.0100    0.0001
   320        0.7895             nan     0.0100    0.0000
   340        0.7823             nan     0.0100   -0.0001
   360        0.7764             nan     0.0100   -0.0001
   380        0.7707             nan     0.0100    0.0000
   400        0.7650             nan     0.0100    0.0000
   420        0.7595             nan     0.0100   -0.0001
   440        0.7543             nan     0.0100   -0.0000
   460        0.7502             nan     0.0100   -0.0002
   480        0.7458             nan     0.0100   -0.0002
   500        0.7413             nan     0.0100   -0.0001
   520        0.7370             nan     0.0100   -0.0002
   540        0.7331             nan     0.0100   -0.0000
   560        0.7293             nan     0.0100   -0.0001
   580        0.7258             nan     0.0100   -0.0001
   600        0.7228             nan     0.0100   -0.0001
   620        0.7189             nan     0.0100   -0.0000
   640        0.7159             nan     0.0100   -0.0001
   660        0.7124             nan     0.0100   -0.0002
   680        0.7091             nan     0.0100   -0.0001
   700        0.7060             nan     0.0100   -0.0002
   720        0.7031             nan     0.0100   -0.0002
   740        0.6997             nan     0.0100   -0.0000
   760        0.6967             nan     0.0100   -0.0001
   780        0.6930             nan     0.0100   -0.0001
   800        0.6896             nan     0.0100   -0.0000
   820        0.6867             nan     0.0100   -0.0001
   840        0.6841             nan     0.0100   -0.0000
   860        0.6816             nan     0.0100   -0.0002
   880        0.6788             nan     0.0100   -0.0001
   900        0.6766             nan     0.0100   -0.0000
   920        0.6742             nan     0.0100    0.0000
   940        0.6714             nan     0.0100   -0.0002
   960        0.6689             nan     0.0100   -0.0002
   980        0.6664             nan     0.0100   -0.0001
  1000        0.6635             nan     0.0100   -0.0001
  1020        0.6609             nan     0.0100   -0.0002
  1040        0.6583             nan     0.0100   -0.0001
  1060        0.6555             nan     0.0100   -0.0001
  1080        0.6527             nan     0.0100   -0.0002
  1100        0.6499             nan     0.0100   -0.0002

- Fold06.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2786             nan     0.1000    0.0260
     2        1.2340             nan     0.1000    0.0216
     3        1.1962             nan     0.1000    0.0191
     4        1.1676             nan     0.1000    0.0140
     5        1.1409             nan     0.1000    0.0131
     6        1.1202             nan     0.1000    0.0107
     7        1.1030             nan     0.1000    0.0079
     8        1.0863             nan     0.1000    0.0072
     9        1.0702             nan     0.1000    0.0071
    10        1.0568             nan     0.1000    0.0050
    20        0.9669             nan     0.1000    0.0026
    40        0.8903             nan     0.1000    0.0005
    60        0.8540             nan     0.1000   -0.0001
    80        0.8267             nan     0.1000   -0.0007
   100        0.8103             nan     0.1000   -0.0003
   120        0.7999             nan     0.1000   -0.0012
   140        0.7896             nan     0.1000   -0.0004
   160        0.7826             nan     0.1000   -0.0012
   180        0.7740             nan     0.1000   -0.0011
   200        0.7678             nan     0.1000   -0.0008
   220        0.7618             nan     0.1000   -0.0005
   240        0.7569             nan     0.1000   -0.0005
   260        0.7534             nan     0.1000   -0.0009
   280        0.7500             nan     0.1000   -0.0002
   300        0.7457             nan     0.1000   -0.0018
   320        0.7407             nan     0.1000   -0.0005
   340        0.7384             nan     0.1000   -0.0007
   360        0.7353             nan     0.1000   -0.0011
   380        0.7325             nan     0.1000   -0.0010
   400        0.7293             nan     0.1000   -0.0001
   420        0.7258             nan     0.1000   -0.0005
   440        0.7217             nan     0.1000   -0.0007
   460        0.7191             nan     0.1000   -0.0006
   480        0.7171             nan     0.1000   -0.0004
   500        0.7164             nan     0.1000   -0.0008
   520        0.7133             nan     0.1000   -0.0020
   540        0.7115             nan     0.1000   -0.0012
   560        0.7090             nan     0.1000   -0.0005
   580        0.7065             nan     0.1000   -0.0008
   600        0.7044             nan     0.1000   -0.0005
   620        0.7027             nan     0.1000   -0.0005
   640        0.7007             nan     0.1000   -0.0001
   660        0.6989             nan     0.1000   -0.0008
   680        0.6976             nan     0.1000   -0.0008
   700        0.6966             nan     0.1000   -0.0007
   720        0.6943             nan     0.1000   -0.0008
   740        0.6922             nan     0.1000   -0.0010
   760        0.6910             nan     0.1000   -0.0007
   780        0.6895             nan     0.1000   -0.0005
   800        0.6873             nan     0.1000   -0.0008
   820        0.6866             nan     0.1000   -0.0004
   840        0.6834             nan     0.1000   -0.0003
   860        0.6836             nan     0.1000   -0.0011
   880        0.6825             nan     0.1000   -0.0020
   900        0.6810             nan     0.1000   -0.0007
   920        0.6792             nan     0.1000   -0.0008
   940        0.6780             nan     0.1000   -0.0009
   960        0.6773             nan     0.1000   -0.0006
   980        0.6760             nan     0.1000   -0.0009
  1000        0.6742             nan     0.1000   -0.0009
  1020        0.6734             nan     0.1000   -0.0003
  1040        0.6702             nan     0.1000   -0.0007
  1060        0.6702             nan     0.1000   -0.0007
  1080        0.6677             nan     0.1000   -0.0007
  1100        0.6672             nan     0.1000   -0.0007

- Fold06.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2644             nan     0.1000    0.0339
     2        1.2102             nan     0.1000    0.0275
     3        1.1648             nan     0.1000    0.0220
     4        1.1233             nan     0.1000    0.0196
     5        1.0898             nan     0.1000    0.0163
     6        1.0616             nan     0.1000    0.0138
     7        1.0357             nan     0.1000    0.0116
     8        1.0130             nan     0.1000    0.0095
     9        0.9941             nan     0.1000    0.0077
    10        0.9762             nan     0.1000    0.0070
    20        0.8852             nan     0.1000    0.0016
    40        0.8129             nan     0.1000   -0.0001
    60        0.7755             nan     0.1000   -0.0001
    80        0.7467             nan     0.1000   -0.0014
   100        0.7320             nan     0.1000   -0.0017
   120        0.7137             nan     0.1000   -0.0011
   140        0.6938             nan     0.1000   -0.0008
   160        0.6827             nan     0.1000   -0.0020
   180        0.6729             nan     0.1000   -0.0010
   200        0.6624             nan     0.1000   -0.0006
   220        0.6516             nan     0.1000   -0.0010
   240        0.6376             nan     0.1000   -0.0001
   260        0.6253             nan     0.1000   -0.0015
   280        0.6157             nan     0.1000   -0.0011
   300        0.6041             nan     0.1000   -0.0005
   320        0.5961             nan     0.1000   -0.0008
   340        0.5881             nan     0.1000   -0.0010
   360        0.5794             nan     0.1000   -0.0009
   380        0.5713             nan     0.1000   -0.0010
   400        0.5655             nan     0.1000   -0.0008
   420        0.5572             nan     0.1000   -0.0006
   440        0.5501             nan     0.1000   -0.0011
   460        0.5433             nan     0.1000   -0.0010
   480        0.5359             nan     0.1000   -0.0008
   500        0.5301             nan     0.1000   -0.0016
   520        0.5233             nan     0.1000   -0.0014
   540        0.5177             nan     0.1000   -0.0006
   560        0.5136             nan     0.1000   -0.0009
   580        0.5074             nan     0.1000   -0.0006
   600        0.5019             nan     0.1000   -0.0012
   620        0.4966             nan     0.1000   -0.0011
   640        0.4918             nan     0.1000   -0.0005
   660        0.4878             nan     0.1000   -0.0010
   680        0.4821             nan     0.1000   -0.0005
   700        0.4785             nan     0.1000   -0.0012
   720        0.4742             nan     0.1000   -0.0017
   740        0.4696             nan     0.1000   -0.0009
   760        0.4643             nan     0.1000   -0.0009
   780        0.4615             nan     0.1000   -0.0011
   800        0.4571             nan     0.1000   -0.0015
   820        0.4532             nan     0.1000   -0.0008
   840        0.4484             nan     0.1000   -0.0009
   860        0.4442             nan     0.1000   -0.0005
   880        0.4414             nan     0.1000   -0.0009
   900        0.4392             nan     0.1000   -0.0017
   920        0.4350             nan     0.1000   -0.0004
   940        0.4315             nan     0.1000   -0.0006
   960        0.4273             nan     0.1000   -0.0011
   980        0.4247             nan     0.1000   -0.0013
  1000        0.4209             nan     0.1000   -0.0007
  1020        0.4166             nan     0.1000   -0.0010
  1040        0.4136             nan     0.1000   -0.0008
  1060        0.4103             nan     0.1000   -0.0006
  1080        0.4071             nan     0.1000   -0.0009
  1100        0.4046             nan     0.1000   -0.0017

- Fold06.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2568             nan     0.1000    0.0386
     2        1.1981             nan     0.1000    0.0266
     3        1.1487             nan     0.1000    0.0246
     4        1.1067             nan     0.1000    0.0211
     5        1.0711             nan     0.1000    0.0176
     6        1.0372             nan     0.1000    0.0150
     7        1.0095             nan     0.1000    0.0134
     8        0.9838             nan     0.1000    0.0103
     9        0.9640             nan     0.1000    0.0087
    10        0.9436             nan     0.1000    0.0071
    20        0.8505             nan     0.1000    0.0013
    40        0.7725             nan     0.1000   -0.0000
    60        0.7307             nan     0.1000   -0.0006
    80        0.6984             nan     0.1000   -0.0009
   100        0.6731             nan     0.1000   -0.0010
   120        0.6494             nan     0.1000   -0.0008
   140        0.6267             nan     0.1000   -0.0019
   160        0.6036             nan     0.1000   -0.0014
   180        0.5850             nan     0.1000   -0.0013
   200        0.5698             nan     0.1000   -0.0016
   220        0.5518             nan     0.1000   -0.0016
   240        0.5365             nan     0.1000   -0.0016
   260        0.5266             nan     0.1000   -0.0002
   280        0.5134             nan     0.1000   -0.0008
   300        0.5020             nan     0.1000   -0.0006
   320        0.4904             nan     0.1000   -0.0011
   340        0.4793             nan     0.1000   -0.0010
   360        0.4686             nan     0.1000   -0.0011
   380        0.4594             nan     0.1000   -0.0014
   400        0.4507             nan     0.1000   -0.0010
   420        0.4425             nan     0.1000   -0.0008
   440        0.4350             nan     0.1000   -0.0014
   460        0.4259             nan     0.1000   -0.0010
   480        0.4186             nan     0.1000   -0.0011
   500        0.4126             nan     0.1000   -0.0009
   520        0.4036             nan     0.1000   -0.0014
   540        0.3973             nan     0.1000   -0.0015
   560        0.3906             nan     0.1000   -0.0008
   580        0.3827             nan     0.1000   -0.0009
   600        0.3770             nan     0.1000   -0.0013
   620        0.3729             nan     0.1000   -0.0012
   640        0.3658             nan     0.1000   -0.0011
   660        0.3604             nan     0.1000   -0.0007
   680        0.3543             nan     0.1000   -0.0009
   700        0.3493             nan     0.1000   -0.0009
   720        0.3447             nan     0.1000   -0.0007
   740        0.3404             nan     0.1000   -0.0012
   760        0.3357             nan     0.1000   -0.0009
   780        0.3310             nan     0.1000   -0.0012
   800        0.3265             nan     0.1000   -0.0008
   820        0.3212             nan     0.1000   -0.0010
   840        0.3171             nan     0.1000   -0.0010
   860        0.3135             nan     0.1000   -0.0010
   880        0.3097             nan     0.1000   -0.0015
   900        0.3050             nan     0.1000   -0.0013
   920        0.3010             nan     0.1000   -0.0015
   940        0.2983             nan     0.1000   -0.0011
   960        0.2950             nan     0.1000   -0.0005
   980        0.2905             nan     0.1000   -0.0006
  1000        0.2862             nan     0.1000   -0.0008
  1020        0.2825             nan     0.1000   -0.0007
  1040        0.2794             nan     0.1000   -0.0012
  1060        0.2768             nan     0.1000   -0.0008
  1080        0.2740             nan     0.1000   -0.0011
  1100        0.2705             nan     0.1000   -0.0008

- Fold06.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3259             nan     0.0100    0.0031
     2        1.3203             nan     0.0100    0.0030
     3        1.3141             nan     0.0100    0.0029
     4        1.3080             nan     0.0100    0.0029
     5        1.3027             nan     0.0100    0.0028
     6        1.2971             nan     0.0100    0.0028
     7        1.2910             nan     0.0100    0.0028
     8        1.2857             nan     0.0100    0.0027
     9        1.2801             nan     0.0100    0.0026
    10        1.2749             nan     0.0100    0.0026
    20        1.2280             nan     0.0100    0.0021
    40        1.1556             nan     0.0100    0.0016
    60        1.1063             nan     0.0100    0.0011
    80        1.0664             nan     0.0100    0.0009
   100        1.0351             nan     0.0100    0.0005
   120        1.0085             nan     0.0100    0.0005
   140        0.9870             nan     0.0100    0.0004
   160        0.9679             nan     0.0100    0.0003
   180        0.9520             nan     0.0100    0.0003
   200        0.9379             nan     0.0100    0.0002
   220        0.9256             nan     0.0100    0.0003
   240        0.9144             nan     0.0100    0.0002
   260        0.9043             nan     0.0100    0.0002
   280        0.8953             nan     0.0100    0.0001
   300        0.8869             nan     0.0100    0.0000
   320        0.8794             nan     0.0100   -0.0000
   340        0.8723             nan     0.0100    0.0001
   360        0.8655             nan     0.0100    0.0001
   380        0.8600             nan     0.0100    0.0001
   400        0.8545             nan     0.0100    0.0000
   420        0.8493             nan     0.0100    0.0000
   440        0.8440             nan     0.0100   -0.0000
   460        0.8391             nan     0.0100   -0.0001
   480        0.8345             nan     0.0100    0.0000
   500        0.8301             nan     0.0100    0.0000
   520        0.8259             nan     0.0100   -0.0000
   540        0.8224             nan     0.0100    0.0000
   560        0.8189             nan     0.0100    0.0000
   580        0.8154             nan     0.0100   -0.0001
   600        0.8121             nan     0.0100    0.0000
   620        0.8088             nan     0.0100    0.0000
   640        0.8058             nan     0.0100    0.0000
   660        0.8028             nan     0.0100   -0.0001
   680        0.7999             nan     0.0100    0.0000
   700        0.7971             nan     0.0100   -0.0000
   720        0.7947             nan     0.0100   -0.0000
   740        0.7923             nan     0.0100   -0.0000
   760        0.7899             nan     0.0100   -0.0000
   780        0.7879             nan     0.0100   -0.0000
   800        0.7857             nan     0.0100   -0.0000
   820        0.7836             nan     0.0100   -0.0001
   840        0.7816             nan     0.0100   -0.0001
   860        0.7797             nan     0.0100   -0.0001
   880        0.7777             nan     0.0100    0.0000
   900        0.7761             nan     0.0100   -0.0000
   920        0.7743             nan     0.0100   -0.0000
   940        0.7725             nan     0.0100   -0.0000
   960        0.7708             nan     0.0100   -0.0000
   980        0.7693             nan     0.0100   -0.0001
  1000        0.7679             nan     0.0100   -0.0000
  1020        0.7667             nan     0.0100   -0.0001
  1040        0.7651             nan     0.0100    0.0000
  1060        0.7635             nan     0.0100    0.0000
  1080        0.7619             nan     0.0100   -0.0001
  1100        0.7603             nan     0.0100   -0.0000

- Fold07.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3237             nan     0.0100    0.0037
     2        1.3162             nan     0.0100    0.0034
     3        1.3087             nan     0.0100    0.0037
     4        1.3016             nan     0.0100    0.0035
     5        1.2941             nan     0.0100    0.0036
     6        1.2877             nan     0.0100    0.0034
     7        1.2813             nan     0.0100    0.0035
     8        1.2745             nan     0.0100    0.0034
     9        1.2678             nan     0.0100    0.0031
    10        1.2614             nan     0.0100    0.0030
    20        1.2015             nan     0.0100    0.0028
    40        1.1101             nan     0.0100    0.0017
    60        1.0438             nan     0.0100    0.0014
    80        0.9931             nan     0.0100    0.0010
   100        0.9541             nan     0.0100    0.0008
   120        0.9245             nan     0.0100    0.0004
   140        0.8988             nan     0.0100    0.0004
   160        0.8791             nan     0.0100    0.0003
   180        0.8622             nan     0.0100    0.0003
   200        0.8479             nan     0.0100    0.0003
   220        0.8365             nan     0.0100    0.0001
   240        0.8257             nan     0.0100    0.0001
   260        0.8154             nan     0.0100    0.0001
   280        0.8065             nan     0.0100    0.0001
   300        0.7988             nan     0.0100    0.0000
   320        0.7914             nan     0.0100   -0.0001
   340        0.7850             nan     0.0100    0.0001
   360        0.7785             nan     0.0100   -0.0000
   380        0.7730             nan     0.0100   -0.0000
   400        0.7675             nan     0.0100    0.0001
   420        0.7623             nan     0.0100   -0.0000
   440        0.7574             nan     0.0100   -0.0001
   460        0.7528             nan     0.0100   -0.0000
   480        0.7487             nan     0.0100   -0.0001
   500        0.7447             nan     0.0100   -0.0002
   520        0.7406             nan     0.0100   -0.0000
   540        0.7372             nan     0.0100    0.0001
   560        0.7336             nan     0.0100    0.0000
   580        0.7298             nan     0.0100   -0.0001
   600        0.7263             nan     0.0100   -0.0000
   620        0.7230             nan     0.0100   -0.0001
   640        0.7195             nan     0.0100   -0.0000
   660        0.7168             nan     0.0100   -0.0001
   680        0.7143             nan     0.0100   -0.0000
   700        0.7115             nan     0.0100   -0.0001
   720        0.7089             nan     0.0100   -0.0000
   740        0.7066             nan     0.0100   -0.0001
   760        0.7042             nan     0.0100   -0.0001
   780        0.7020             nan     0.0100   -0.0001
   800        0.6996             nan     0.0100   -0.0002
   820        0.6976             nan     0.0100   -0.0000
   840        0.6950             nan     0.0100   -0.0001
   860        0.6930             nan     0.0100   -0.0000
   880        0.6908             nan     0.0100   -0.0000
   900        0.6886             nan     0.0100   -0.0000
   920        0.6864             nan     0.0100   -0.0001
   940        0.6847             nan     0.0100   -0.0001
   960        0.6827             nan     0.0100   -0.0002
   980        0.6808             nan     0.0100   -0.0001
  1000        0.6788             nan     0.0100   -0.0001
  1020        0.6767             nan     0.0100   -0.0001
  1040        0.6750             nan     0.0100   -0.0000
  1060        0.6735             nan     0.0100   -0.0001
  1080        0.6715             nan     0.0100   -0.0001
  1100        0.6695             nan     0.0100   -0.0001

- Fold07.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3234             nan     0.0100    0.0040
     2        1.3149             nan     0.0100    0.0045
     3        1.3070             nan     0.0100    0.0038
     4        1.2992             nan     0.0100    0.0036
     5        1.2914             nan     0.0100    0.0038
     6        1.2834             nan     0.0100    0.0039
     7        1.2756             nan     0.0100    0.0038
     8        1.2686             nan     0.0100    0.0037
     9        1.2611             nan     0.0100    0.0035
    10        1.2534             nan     0.0100    0.0034
    20        1.1886             nan     0.0100    0.0030
    40        1.0869             nan     0.0100    0.0021
    60        1.0143             nan     0.0100    0.0013
    80        0.9587             nan     0.0100    0.0011
   100        0.9179             nan     0.0100    0.0008
   120        0.8864             nan     0.0100    0.0005
   140        0.8609             nan     0.0100    0.0005
   160        0.8414             nan     0.0100    0.0003
   180        0.8249             nan     0.0100    0.0003
   200        0.8109             nan     0.0100    0.0002
   220        0.7981             nan     0.0100   -0.0000
   240        0.7868             nan     0.0100    0.0002
   260        0.7775             nan     0.0100   -0.0001
   280        0.7689             nan     0.0100   -0.0001
   300        0.7612             nan     0.0100   -0.0001
   320        0.7540             nan     0.0100   -0.0000
   340        0.7470             nan     0.0100   -0.0000
   360        0.7402             nan     0.0100   -0.0000
   380        0.7343             nan     0.0100   -0.0002
   400        0.7289             nan     0.0100   -0.0002
   420        0.7228             nan     0.0100    0.0001
   440        0.7172             nan     0.0100   -0.0000
   460        0.7119             nan     0.0100   -0.0001
   480        0.7071             nan     0.0100    0.0000
   500        0.7030             nan     0.0100   -0.0001
   520        0.6987             nan     0.0100   -0.0001
   540        0.6949             nan     0.0100   -0.0001
   560        0.6908             nan     0.0100   -0.0000
   580        0.6870             nan     0.0100   -0.0001
   600        0.6836             nan     0.0100   -0.0001
   620        0.6801             nan     0.0100   -0.0002
   640        0.6770             nan     0.0100   -0.0001
   660        0.6737             nan     0.0100   -0.0001
   680        0.6705             nan     0.0100   -0.0002
   700        0.6671             nan     0.0100   -0.0001
   720        0.6643             nan     0.0100   -0.0000
   740        0.6614             nan     0.0100   -0.0001
   760        0.6582             nan     0.0100   -0.0001
   780        0.6551             nan     0.0100   -0.0002
   800        0.6524             nan     0.0100   -0.0003
   820        0.6494             nan     0.0100   -0.0001
   840        0.6461             nan     0.0100   -0.0001
   860        0.6433             nan     0.0100   -0.0001
   880        0.6406             nan     0.0100   -0.0001
   900        0.6382             nan     0.0100   -0.0001
   920        0.6352             nan     0.0100   -0.0001
   940        0.6330             nan     0.0100   -0.0002
   960        0.6308             nan     0.0100   -0.0002
   980        0.6289             nan     0.0100   -0.0001
  1000        0.6263             nan     0.0100   -0.0001
  1020        0.6242             nan     0.0100   -0.0002
  1040        0.6217             nan     0.0100   -0.0001
  1060        0.6190             nan     0.0100   -0.0001
  1080        0.6162             nan     0.0100   -0.0001
  1100        0.6139             nan     0.0100   -0.0001

- Fold07.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2666             nan     0.1000    0.0291
     2        1.2229             nan     0.1000    0.0242
     3        1.1829             nan     0.1000    0.0198
     4        1.1525             nan     0.1000    0.0166
     5        1.1244             nan     0.1000    0.0135
     6        1.1008             nan     0.1000    0.0114
     7        1.0806             nan     0.1000    0.0091
     8        1.0609             nan     0.1000    0.0080
     9        1.0461             nan     0.1000    0.0067
    10        1.0334             nan     0.1000    0.0055
    20        0.9362             nan     0.1000    0.0023
    40        0.8529             nan     0.1000    0.0003
    60        0.8116             nan     0.1000    0.0000
    80        0.7867             nan     0.1000   -0.0005
   100        0.7690             nan     0.1000   -0.0008
   120        0.7569             nan     0.1000   -0.0007
   140        0.7461             nan     0.1000   -0.0008
   160        0.7376             nan     0.1000   -0.0006
   180        0.7304             nan     0.1000   -0.0006
   200        0.7239             nan     0.1000   -0.0011
   220        0.7177             nan     0.1000   -0.0004
   240        0.7111             nan     0.1000   -0.0004
   260        0.7065             nan     0.1000   -0.0005
   280        0.7030             nan     0.1000   -0.0010
   300        0.6989             nan     0.1000   -0.0015
   320        0.6937             nan     0.1000   -0.0005
   340        0.6897             nan     0.1000   -0.0000
   360        0.6871             nan     0.1000   -0.0005
   380        0.6836             nan     0.1000   -0.0004
   400        0.6818             nan     0.1000   -0.0009
   420        0.6782             nan     0.1000   -0.0010
   440        0.6759             nan     0.1000   -0.0003
   460        0.6741             nan     0.1000   -0.0007
   480        0.6708             nan     0.1000   -0.0002
   500        0.6693             nan     0.1000   -0.0012
   520        0.6671             nan     0.1000   -0.0011
   540        0.6646             nan     0.1000   -0.0011
   560        0.6626             nan     0.1000   -0.0006
   580        0.6606             nan     0.1000   -0.0013
   600        0.6583             nan     0.1000   -0.0009
   620        0.6573             nan     0.1000   -0.0009
   640        0.6538             nan     0.1000   -0.0006
   660        0.6520             nan     0.1000   -0.0003
   680        0.6503             nan     0.1000   -0.0001
   700        0.6491             nan     0.1000   -0.0004
   720        0.6462             nan     0.1000   -0.0009
   740        0.6438             nan     0.1000   -0.0005
   760        0.6414             nan     0.1000   -0.0004
   780        0.6404             nan     0.1000   -0.0014
   800        0.6389             nan     0.1000   -0.0005
   820        0.6372             nan     0.1000   -0.0007
   840        0.6354             nan     0.1000   -0.0001
   860        0.6343             nan     0.1000   -0.0015
   880        0.6335             nan     0.1000   -0.0011
   900        0.6318             nan     0.1000   -0.0006
   920        0.6304             nan     0.1000   -0.0005
   940        0.6296             nan     0.1000   -0.0014
   960        0.6287             nan     0.1000   -0.0003
   980        0.6275             nan     0.1000   -0.0009
  1000        0.6261             nan     0.1000   -0.0004
  1020        0.6248             nan     0.1000   -0.0011
  1040        0.6233             nan     0.1000   -0.0004
  1060        0.6223             nan     0.1000   -0.0008
  1080        0.6209             nan     0.1000   -0.0002
  1100        0.6200             nan     0.1000   -0.0006

- Fold07.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2543             nan     0.1000    0.0366
     2        1.1905             nan     0.1000    0.0310
     3        1.1441             nan     0.1000    0.0216
     4        1.1024             nan     0.1000    0.0200
     5        1.0670             nan     0.1000    0.0172
     6        1.0335             nan     0.1000    0.0158
     7        1.0063             nan     0.1000    0.0123
     8        0.9828             nan     0.1000    0.0104
     9        0.9637             nan     0.1000    0.0082
    10        0.9461             nan     0.1000    0.0091
    20        0.8480             nan     0.1000    0.0019
    40        0.7695             nan     0.1000   -0.0000
    60        0.7300             nan     0.1000    0.0001
    80        0.7045             nan     0.1000   -0.0011
   100        0.6827             nan     0.1000   -0.0001
   120        0.6636             nan     0.1000   -0.0010
   140        0.6517             nan     0.1000   -0.0007
   160        0.6363             nan     0.1000   -0.0015
   180        0.6289             nan     0.1000   -0.0004
   200        0.6173             nan     0.1000   -0.0014
   220        0.6038             nan     0.1000   -0.0008
   240        0.5925             nan     0.1000   -0.0006
   260        0.5832             nan     0.1000   -0.0010
   280        0.5757             nan     0.1000   -0.0014
   300        0.5652             nan     0.1000   -0.0004
   320        0.5573             nan     0.1000   -0.0019
   340        0.5496             nan     0.1000   -0.0010
   360        0.5451             nan     0.1000   -0.0008
   380        0.5360             nan     0.1000   -0.0009
   400        0.5279             nan     0.1000   -0.0012
   420        0.5210             nan     0.1000   -0.0021
   440        0.5134             nan     0.1000   -0.0013
   460        0.5048             nan     0.1000   -0.0002
   480        0.4988             nan     0.1000   -0.0014
   500        0.4924             nan     0.1000   -0.0008
   520        0.4882             nan     0.1000   -0.0011
   540        0.4828             nan     0.1000   -0.0003
   560        0.4759             nan     0.1000   -0.0007
   580        0.4700             nan     0.1000   -0.0009
   600        0.4673             nan     0.1000   -0.0007
   620        0.4619             nan     0.1000   -0.0011
   640        0.4601             nan     0.1000   -0.0013
   660        0.4538             nan     0.1000   -0.0007
   680        0.4482             nan     0.1000   -0.0018
   700        0.4431             nan     0.1000   -0.0017
   720        0.4386             nan     0.1000   -0.0007
   740        0.4353             nan     0.1000   -0.0008
   760        0.4313             nan     0.1000   -0.0006
   780        0.4283             nan     0.1000   -0.0009
   800        0.4241             nan     0.1000   -0.0006
   820        0.4199             nan     0.1000   -0.0015
   840        0.4167             nan     0.1000   -0.0011
   860        0.4137             nan     0.1000   -0.0009
   880        0.4110             nan     0.1000   -0.0003
   900        0.4089             nan     0.1000   -0.0009
   920        0.4049             nan     0.1000   -0.0010
   940        0.4008             nan     0.1000   -0.0019
   960        0.3971             nan     0.1000   -0.0003
   980        0.3946             nan     0.1000   -0.0006
  1000        0.3916             nan     0.1000   -0.0015
  1020        0.3887             nan     0.1000   -0.0014
  1040        0.3872             nan     0.1000   -0.0007
  1060        0.3836             nan     0.1000   -0.0007
  1080        0.3776             nan     0.1000   -0.0004
  1100        0.3746             nan     0.1000   -0.0005

- Fold07.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2508             nan     0.1000    0.0398
     2        1.1812             nan     0.1000    0.0296
     3        1.1247             nan     0.1000    0.0270
     4        1.0767             nan     0.1000    0.0223
     5        1.0328             nan     0.1000    0.0190
     6        0.9984             nan     0.1000    0.0153
     7        0.9705             nan     0.1000    0.0130
     8        0.9461             nan     0.1000    0.0117
     9        0.9265             nan     0.1000    0.0087
    10        0.9092             nan     0.1000    0.0062
    20        0.8045             nan     0.1000    0.0014
    40        0.7274             nan     0.1000    0.0003
    60        0.6859             nan     0.1000   -0.0008
    80        0.6504             nan     0.1000   -0.0004
   100        0.6250             nan     0.1000   -0.0009
   120        0.6044             nan     0.1000   -0.0008
   140        0.5838             nan     0.1000   -0.0009
   160        0.5652             nan     0.1000   -0.0014
   180        0.5503             nan     0.1000   -0.0003
   200        0.5348             nan     0.1000   -0.0019
   220        0.5191             nan     0.1000   -0.0011
   240        0.5068             nan     0.1000   -0.0004
   260        0.4921             nan     0.1000   -0.0004
   280        0.4821             nan     0.1000   -0.0016
   300        0.4728             nan     0.1000   -0.0014
   320        0.4603             nan     0.1000   -0.0010
   340        0.4529             nan     0.1000   -0.0006
   360        0.4420             nan     0.1000   -0.0011
   380        0.4319             nan     0.1000   -0.0013
   400        0.4212             nan     0.1000   -0.0006
   420        0.4124             nan     0.1000   -0.0018
   440        0.4054             nan     0.1000   -0.0015
   460        0.3965             nan     0.1000   -0.0011
   480        0.3897             nan     0.1000   -0.0013
   500        0.3809             nan     0.1000   -0.0007
   520        0.3754             nan     0.1000   -0.0010
   540        0.3690             nan     0.1000   -0.0009
   560        0.3612             nan     0.1000   -0.0007
   580        0.3548             nan     0.1000   -0.0011
   600        0.3491             nan     0.1000   -0.0015
   620        0.3432             nan     0.1000   -0.0005
   640        0.3377             nan     0.1000   -0.0005
   660        0.3320             nan     0.1000   -0.0009
   680        0.3279             nan     0.1000   -0.0004
   700        0.3208             nan     0.1000   -0.0007
   720        0.3169             nan     0.1000   -0.0013
   740        0.3140             nan     0.1000   -0.0012
   760        0.3095             nan     0.1000   -0.0009
   780        0.3044             nan     0.1000   -0.0017
   800        0.3005             nan     0.1000   -0.0011
   820        0.2961             nan     0.1000   -0.0011
   840        0.2920             nan     0.1000   -0.0010
   860        0.2889             nan     0.1000   -0.0011
   880        0.2850             nan     0.1000   -0.0018
   900        0.2799             nan     0.1000   -0.0011
   920        0.2760             nan     0.1000   -0.0006
   940        0.2730             nan     0.1000   -0.0010
   960        0.2702             nan     0.1000   -0.0005
   980        0.2673             nan     0.1000   -0.0009
  1000        0.2637             nan     0.1000   -0.0009
  1020        0.2612             nan     0.1000   -0.0010
  1040        0.2579             nan     0.1000   -0.0005
  1060        0.2551             nan     0.1000   -0.0005
  1080        0.2526             nan     0.1000   -0.0009
  1100        0.2489             nan     0.1000   -0.0006

- Fold07.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3259             nan     0.0100    0.0027
     2        1.3205             nan     0.0100    0.0028
     3        1.3146             nan     0.0100    0.0026
     4        1.3089             nan     0.0100    0.0028
     5        1.3036             nan     0.0100    0.0027
     6        1.2984             nan     0.0100    0.0025
     7        1.2927             nan     0.0100    0.0027
     8        1.2871             nan     0.0100    0.0026
     9        1.2820             nan     0.0100    0.0024
    10        1.2766             nan     0.0100    0.0024
    20        1.2339             nan     0.0100    0.0020
    40        1.1671             nan     0.0100    0.0008
    60        1.1167             nan     0.0100    0.0009
    80        1.0814             nan     0.0100    0.0006
   100        1.0528             nan     0.0100    0.0004
   120        1.0276             nan     0.0100    0.0005
   140        1.0069             nan     0.0100    0.0004
   160        0.9899             nan     0.0100    0.0003
   180        0.9756             nan     0.0100    0.0003
   200        0.9633             nan     0.0100    0.0003
   220        0.9516             nan     0.0100    0.0001
   240        0.9416             nan     0.0100    0.0002
   260        0.9329             nan     0.0100    0.0002
   280        0.9252             nan     0.0100   -0.0001
   300        0.9177             nan     0.0100   -0.0000
   320        0.9111             nan     0.0100    0.0001
   340        0.9055             nan     0.0100    0.0001
   360        0.8998             nan     0.0100    0.0001
   380        0.8948             nan     0.0100   -0.0001
   400        0.8900             nan     0.0100    0.0001
   420        0.8854             nan     0.0100    0.0000
   440        0.8808             nan     0.0100    0.0000
   460        0.8765             nan     0.0100    0.0001
   480        0.8728             nan     0.0100    0.0001
   500        0.8690             nan     0.0100   -0.0000
   520        0.8653             nan     0.0100    0.0000
   540        0.8614             nan     0.0100   -0.0000
   560        0.8578             nan     0.0100   -0.0001
   580        0.8548             nan     0.0100   -0.0000
   600        0.8516             nan     0.0100    0.0000
   620        0.8487             nan     0.0100    0.0000
   640        0.8457             nan     0.0100   -0.0000
   660        0.8428             nan     0.0100   -0.0000
   680        0.8404             nan     0.0100   -0.0000
   700        0.8380             nan     0.0100   -0.0000
   720        0.8355             nan     0.0100   -0.0002
   740        0.8333             nan     0.0100    0.0000
   760        0.8311             nan     0.0100    0.0000
   780        0.8289             nan     0.0100    0.0000
   800        0.8268             nan     0.0100   -0.0002
   820        0.8247             nan     0.0100   -0.0000
   840        0.8228             nan     0.0100   -0.0001
   860        0.8209             nan     0.0100    0.0000
   880        0.8190             nan     0.0100   -0.0001
   900        0.8170             nan     0.0100    0.0000
   920        0.8154             nan     0.0100   -0.0001
   940        0.8138             nan     0.0100   -0.0000
   960        0.8122             nan     0.0100   -0.0001
   980        0.8106             nan     0.0100   -0.0000
  1000        0.8092             nan     0.0100   -0.0000
  1020        0.8075             nan     0.0100   -0.0000
  1040        0.8062             nan     0.0100   -0.0000
  1060        0.8048             nan     0.0100   -0.0000
  1080        0.8032             nan     0.0100   -0.0000
  1100        0.8020             nan     0.0100   -0.0001

- Fold08.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0034
     2        1.3181             nan     0.0100    0.0033
     3        1.3111             nan     0.0100    0.0030
     4        1.3047             nan     0.0100    0.0033
     5        1.2984             nan     0.0100    0.0033
     6        1.2917             nan     0.0100    0.0032
     7        1.2857             nan     0.0100    0.0030
     8        1.2795             nan     0.0100    0.0029
     9        1.2733             nan     0.0100    0.0030
    10        1.2672             nan     0.0100    0.0029
    20        1.2113             nan     0.0100    0.0024
    40        1.1277             nan     0.0100    0.0018
    60        1.0651             nan     0.0100    0.0013
    80        1.0186             nan     0.0100    0.0010
   100        0.9819             nan     0.0100    0.0003
   120        0.9553             nan     0.0100    0.0003
   140        0.9332             nan     0.0100    0.0003
   160        0.9150             nan     0.0100    0.0003
   180        0.9004             nan     0.0100    0.0003
   200        0.8860             nan     0.0100    0.0003
   220        0.8748             nan     0.0100    0.0001
   240        0.8646             nan     0.0100    0.0000
   260        0.8550             nan     0.0100    0.0001
   280        0.8468             nan     0.0100    0.0001
   300        0.8396             nan     0.0100    0.0001
   320        0.8327             nan     0.0100    0.0001
   340        0.8267             nan     0.0100    0.0001
   360        0.8202             nan     0.0100   -0.0001
   380        0.8146             nan     0.0100    0.0001
   400        0.8097             nan     0.0100    0.0000
   420        0.8044             nan     0.0100   -0.0000
   440        0.7996             nan     0.0100   -0.0001
   460        0.7956             nan     0.0100   -0.0001
   480        0.7914             nan     0.0100   -0.0001
   500        0.7874             nan     0.0100   -0.0000
   520        0.7835             nan     0.0100    0.0000
   540        0.7800             nan     0.0100   -0.0001
   560        0.7765             nan     0.0100   -0.0000
   580        0.7732             nan     0.0100   -0.0001
   600        0.7697             nan     0.0100   -0.0000
   620        0.7664             nan     0.0100   -0.0001
   640        0.7632             nan     0.0100   -0.0000
   660        0.7603             nan     0.0100   -0.0000
   680        0.7572             nan     0.0100   -0.0001
   700        0.7542             nan     0.0100   -0.0000
   720        0.7510             nan     0.0100   -0.0001
   740        0.7483             nan     0.0100   -0.0001
   760        0.7463             nan     0.0100   -0.0001
   780        0.7442             nan     0.0100   -0.0001
   800        0.7421             nan     0.0100   -0.0001
   820        0.7397             nan     0.0100   -0.0001
   840        0.7370             nan     0.0100    0.0000
   860        0.7345             nan     0.0100   -0.0001
   880        0.7323             nan     0.0100   -0.0001
   900        0.7301             nan     0.0100   -0.0001
   920        0.7282             nan     0.0100   -0.0001
   940        0.7261             nan     0.0100   -0.0001
   960        0.7237             nan     0.0100   -0.0001
   980        0.7220             nan     0.0100   -0.0001
  1000        0.7199             nan     0.0100   -0.0001
  1020        0.7179             nan     0.0100   -0.0000
  1040        0.7163             nan     0.0100   -0.0001
  1060        0.7144             nan     0.0100   -0.0001
  1080        0.7123             nan     0.0100   -0.0000
  1100        0.7109             nan     0.0100   -0.0001

- Fold08.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0038
     2        1.3167             nan     0.0100    0.0036
     3        1.3089             nan     0.0100    0.0039
     4        1.3019             nan     0.0100    0.0036
     5        1.2953             nan     0.0100    0.0032
     6        1.2881             nan     0.0100    0.0033
     7        1.2809             nan     0.0100    0.0032
     8        1.2739             nan     0.0100    0.0035
     9        1.2671             nan     0.0100    0.0032
    10        1.2603             nan     0.0100    0.0032
    20        1.1998             nan     0.0100    0.0024
    40        1.1048             nan     0.0100    0.0021
    60        1.0378             nan     0.0100    0.0014
    80        0.9865             nan     0.0100    0.0009
   100        0.9489             nan     0.0100    0.0005
   120        0.9196             nan     0.0100    0.0005
   140        0.8956             nan     0.0100    0.0005
   160        0.8773             nan     0.0100    0.0002
   180        0.8614             nan     0.0100    0.0003
   200        0.8482             nan     0.0100    0.0001
   220        0.8355             nan     0.0100    0.0001
   240        0.8246             nan     0.0100   -0.0000
   260        0.8151             nan     0.0100    0.0001
   280        0.8067             nan     0.0100    0.0000
   300        0.7983             nan     0.0100    0.0000
   320        0.7907             nan     0.0100   -0.0000
   340        0.7842             nan     0.0100   -0.0001
   360        0.7780             nan     0.0100   -0.0001
   380        0.7722             nan     0.0100   -0.0001
   400        0.7665             nan     0.0100   -0.0001
   420        0.7611             nan     0.0100    0.0000
   440        0.7558             nan     0.0100   -0.0000
   460        0.7507             nan     0.0100   -0.0000
   480        0.7459             nan     0.0100   -0.0000
   500        0.7408             nan     0.0100   -0.0000
   520        0.7363             nan     0.0100   -0.0000
   540        0.7321             nan     0.0100   -0.0002
   560        0.7278             nan     0.0100   -0.0001
   580        0.7242             nan     0.0100   -0.0001
   600        0.7205             nan     0.0100   -0.0001
   620        0.7167             nan     0.0100    0.0000
   640        0.7132             nan     0.0100   -0.0001
   660        0.7094             nan     0.0100   -0.0001
   680        0.7054             nan     0.0100   -0.0001
   700        0.7022             nan     0.0100   -0.0002
   720        0.6990             nan     0.0100   -0.0001
   740        0.6957             nan     0.0100   -0.0001
   760        0.6924             nan     0.0100   -0.0002
   780        0.6893             nan     0.0100   -0.0001
   800        0.6861             nan     0.0100   -0.0001
   820        0.6834             nan     0.0100   -0.0001
   840        0.6803             nan     0.0100   -0.0002
   860        0.6773             nan     0.0100   -0.0000
   880        0.6741             nan     0.0100   -0.0001
   900        0.6710             nan     0.0100   -0.0000
   920        0.6684             nan     0.0100   -0.0000
   940        0.6659             nan     0.0100   -0.0001
   960        0.6630             nan     0.0100   -0.0000
   980        0.6604             nan     0.0100   -0.0000
  1000        0.6579             nan     0.0100   -0.0001
  1020        0.6556             nan     0.0100   -0.0001
  1040        0.6530             nan     0.0100   -0.0001
  1060        0.6512             nan     0.0100   -0.0002
  1080        0.6483             nan     0.0100    0.0001
  1100        0.6453             nan     0.0100   -0.0002

- Fold08.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2749             nan     0.1000    0.0278
     2        1.2317             nan     0.1000    0.0227
     3        1.2004             nan     0.1000    0.0182
     4        1.1667             nan     0.1000    0.0162
     5        1.1385             nan     0.1000    0.0125
     6        1.1161             nan     0.1000    0.0108
     7        1.0981             nan     0.1000    0.0091
     8        1.0840             nan     0.1000    0.0078
     9        1.0684             nan     0.1000    0.0069
    10        1.0544             nan     0.1000    0.0060
    20        0.9618             nan     0.1000    0.0010
    40        0.8876             nan     0.1000   -0.0000
    60        0.8496             nan     0.1000   -0.0002
    80        0.8272             nan     0.1000    0.0001
   100        0.8095             nan     0.1000   -0.0007
   120        0.7981             nan     0.1000   -0.0004
   140        0.7870             nan     0.1000   -0.0015
   160        0.7797             nan     0.1000   -0.0006
   180        0.7716             nan     0.1000   -0.0015
   200        0.7646             nan     0.1000   -0.0001
   220        0.7570             nan     0.1000   -0.0008
   240        0.7521             nan     0.1000   -0.0013
   260        0.7472             nan     0.1000    0.0001
   280        0.7440             nan     0.1000   -0.0006
   300        0.7392             nan     0.1000   -0.0010
   320        0.7351             nan     0.1000   -0.0008
   340        0.7316             nan     0.1000   -0.0007
   360        0.7271             nan     0.1000   -0.0007
   380        0.7240             nan     0.1000   -0.0006
   400        0.7207             nan     0.1000   -0.0007
   420        0.7179             nan     0.1000   -0.0004
   440        0.7145             nan     0.1000   -0.0004
   460        0.7125             nan     0.1000   -0.0004
   480        0.7085             nan     0.1000   -0.0007
   500        0.7076             nan     0.1000   -0.0007
   520        0.7051             nan     0.1000   -0.0007
   540        0.7026             nan     0.1000   -0.0009
   560        0.6997             nan     0.1000   -0.0007
   580        0.6964             nan     0.1000   -0.0003
   600        0.6947             nan     0.1000   -0.0010
   620        0.6918             nan     0.1000   -0.0006
   640        0.6899             nan     0.1000   -0.0005
   660        0.6880             nan     0.1000   -0.0008
   680        0.6863             nan     0.1000   -0.0008
   700        0.6834             nan     0.1000   -0.0010
   720        0.6817             nan     0.1000   -0.0008
   740        0.6811             nan     0.1000   -0.0005
   760        0.6795             nan     0.1000   -0.0008
   780        0.6775             nan     0.1000   -0.0009
   800        0.6753             nan     0.1000   -0.0007
   820        0.6739             nan     0.1000   -0.0010
   840        0.6722             nan     0.1000   -0.0006
   860        0.6693             nan     0.1000   -0.0008
   880        0.6673             nan     0.1000   -0.0004
   900        0.6650             nan     0.1000   -0.0008
   920        0.6635             nan     0.1000   -0.0010
   940        0.6618             nan     0.1000   -0.0008
   960        0.6597             nan     0.1000   -0.0004
   980        0.6583             nan     0.1000   -0.0004
  1000        0.6570             nan     0.1000   -0.0007
  1020        0.6564             nan     0.1000   -0.0011
  1040        0.6536             nan     0.1000   -0.0004
  1060        0.6534             nan     0.1000   -0.0009
  1080        0.6519             nan     0.1000   -0.0014
  1100        0.6514             nan     0.1000   -0.0006

- Fold08.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2651             nan     0.1000    0.0335
     2        1.2074             nan     0.1000    0.0287
     3        1.1628             nan     0.1000    0.0217
     4        1.1244             nan     0.1000    0.0192
     5        1.0921             nan     0.1000    0.0162
     6        1.0608             nan     0.1000    0.0141
     7        1.0330             nan     0.1000    0.0117
     8        1.0131             nan     0.1000    0.0093
     9        0.9941             nan     0.1000    0.0078
    10        0.9782             nan     0.1000    0.0057
    20        0.8915             nan     0.1000   -0.0004
    40        0.8075             nan     0.1000    0.0006
    60        0.7692             nan     0.1000    0.0004
    80        0.7406             nan     0.1000   -0.0005
   100        0.7223             nan     0.1000   -0.0003
   120        0.7054             nan     0.1000   -0.0009
   140        0.6906             nan     0.1000    0.0004
   160        0.6754             nan     0.1000   -0.0011
   180        0.6618             nan     0.1000   -0.0011
   200        0.6497             nan     0.1000   -0.0013
   220        0.6350             nan     0.1000   -0.0003
   240        0.6229             nan     0.1000   -0.0005
   260        0.6134             nan     0.1000   -0.0014
   280        0.6024             nan     0.1000   -0.0008
   300        0.5888             nan     0.1000   -0.0014
   320        0.5792             nan     0.1000   -0.0009
   340        0.5726             nan     0.1000   -0.0006
   360        0.5660             nan     0.1000   -0.0004
   380        0.5582             nan     0.1000   -0.0013
   400        0.5515             nan     0.1000   -0.0004
   420        0.5428             nan     0.1000   -0.0001
   440        0.5367             nan     0.1000   -0.0012
   460        0.5290             nan     0.1000   -0.0008
   480        0.5246             nan     0.1000   -0.0022
   500        0.5204             nan     0.1000   -0.0011
   520        0.5151             nan     0.1000   -0.0012
   540        0.5075             nan     0.1000   -0.0006
   560        0.5021             nan     0.1000   -0.0008
   580        0.4954             nan     0.1000   -0.0011
   600        0.4888             nan     0.1000   -0.0015
   620        0.4849             nan     0.1000   -0.0009
   640        0.4792             nan     0.1000   -0.0011
   660        0.4724             nan     0.1000   -0.0009
   680        0.4662             nan     0.1000   -0.0005
   700        0.4607             nan     0.1000   -0.0008
   720        0.4563             nan     0.1000   -0.0009
   740        0.4514             nan     0.1000   -0.0012
   760        0.4474             nan     0.1000   -0.0014
   780        0.4446             nan     0.1000   -0.0010
   800        0.4425             nan     0.1000   -0.0008
   820        0.4368             nan     0.1000   -0.0006
   840        0.4315             nan     0.1000   -0.0003
   860        0.4280             nan     0.1000   -0.0006
   880        0.4243             nan     0.1000   -0.0006
   900        0.4183             nan     0.1000   -0.0005
   920        0.4133             nan     0.1000   -0.0006
   940        0.4102             nan     0.1000   -0.0007
   960        0.4071             nan     0.1000   -0.0006
   980        0.4025             nan     0.1000   -0.0010
  1000        0.3993             nan     0.1000   -0.0007
  1020        0.3973             nan     0.1000   -0.0010
  1040        0.3940             nan     0.1000   -0.0009
  1060        0.3914             nan     0.1000   -0.0009
  1080        0.3877             nan     0.1000   -0.0007
  1100        0.3839             nan     0.1000   -0.0004

- Fold08.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2531             nan     0.1000    0.0359
     2        1.1865             nan     0.1000    0.0288
     3        1.1348             nan     0.1000    0.0244
     4        1.0943             nan     0.1000    0.0205
     5        1.0586             nan     0.1000    0.0167
     6        1.0272             nan     0.1000    0.0144
     7        0.9995             nan     0.1000    0.0130
     8        0.9772             nan     0.1000    0.0101
     9        0.9562             nan     0.1000    0.0068
    10        0.9406             nan     0.1000    0.0068
    20        0.8486             nan     0.1000    0.0019
    40        0.7721             nan     0.1000    0.0001
    60        0.7258             nan     0.1000   -0.0015
    80        0.6927             nan     0.1000   -0.0019
   100        0.6623             nan     0.1000   -0.0017
   120        0.6394             nan     0.1000   -0.0010
   140        0.6181             nan     0.1000   -0.0007
   160        0.5963             nan     0.1000   -0.0026
   180        0.5794             nan     0.1000   -0.0012
   200        0.5602             nan     0.1000   -0.0008
   220        0.5453             nan     0.1000   -0.0010
   240        0.5287             nan     0.1000   -0.0015
   260        0.5161             nan     0.1000   -0.0015
   280        0.5044             nan     0.1000   -0.0010
   300        0.4929             nan     0.1000   -0.0014
   320        0.4822             nan     0.1000   -0.0005
   340        0.4716             nan     0.1000   -0.0017
   360        0.4565             nan     0.1000   -0.0017
   380        0.4490             nan     0.1000   -0.0025
   400        0.4390             nan     0.1000   -0.0008
   420        0.4274             nan     0.1000   -0.0016
   440        0.4195             nan     0.1000   -0.0015
   460        0.4109             nan     0.1000   -0.0009
   480        0.4023             nan     0.1000   -0.0013
   500        0.3952             nan     0.1000   -0.0012
   520        0.3855             nan     0.1000   -0.0006
   540        0.3783             nan     0.1000   -0.0006
   560        0.3718             nan     0.1000   -0.0005
   580        0.3664             nan     0.1000   -0.0010
   600        0.3609             nan     0.1000   -0.0006
   620        0.3564             nan     0.1000   -0.0010
   640        0.3515             nan     0.1000   -0.0009
   660        0.3458             nan     0.1000   -0.0004
   680        0.3400             nan     0.1000   -0.0007
   700        0.3338             nan     0.1000   -0.0011
   720        0.3296             nan     0.1000   -0.0005
   740        0.3244             nan     0.1000   -0.0006
   760        0.3204             nan     0.1000   -0.0019
   780        0.3154             nan     0.1000   -0.0004
   800        0.3109             nan     0.1000   -0.0008
   820        0.3071             nan     0.1000   -0.0014
   840        0.3022             nan     0.1000   -0.0009
   860        0.2979             nan     0.1000   -0.0013
   880        0.2946             nan     0.1000   -0.0008
   900        0.2896             nan     0.1000   -0.0009
   920        0.2851             nan     0.1000   -0.0013
   940        0.2802             nan     0.1000   -0.0011
   960        0.2764             nan     0.1000   -0.0010
   980        0.2726             nan     0.1000   -0.0006
  1000        0.2704             nan     0.1000   -0.0006
  1020        0.2663             nan     0.1000   -0.0015
  1040        0.2635             nan     0.1000   -0.0010
  1060        0.2591             nan     0.1000   -0.0011
  1080        0.2556             nan     0.1000   -0.0005
  1100        0.2532             nan     0.1000   -0.0007

- Fold08.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3251             nan     0.0100    0.0030
     2        1.3187             nan     0.0100    0.0030
     3        1.3128             nan     0.0100    0.0030
     4        1.3068             nan     0.0100    0.0028
     5        1.3014             nan     0.0100    0.0027
     6        1.2956             nan     0.0100    0.0027
     7        1.2897             nan     0.0100    0.0026
     8        1.2844             nan     0.0100    0.0027
     9        1.2790             nan     0.0100    0.0025
    10        1.2741             nan     0.0100    0.0025
    20        1.2272             nan     0.0100    0.0020
    40        1.1558             nan     0.0100    0.0015
    60        1.1076             nan     0.0100    0.0011
    80        1.0704             nan     0.0100    0.0007
   100        1.0406             nan     0.0100    0.0006
   120        1.0158             nan     0.0100    0.0005
   140        0.9952             nan     0.0100    0.0003
   160        0.9776             nan     0.0100    0.0003
   180        0.9630             nan     0.0100    0.0003
   200        0.9498             nan     0.0100    0.0003
   220        0.9386             nan     0.0100    0.0001
   240        0.9278             nan     0.0100    0.0002
   260        0.9183             nan     0.0100   -0.0000
   280        0.9099             nan     0.0100    0.0001
   300        0.9020             nan     0.0100    0.0001
   320        0.8950             nan     0.0100    0.0002
   340        0.8889             nan     0.0100   -0.0001
   360        0.8831             nan     0.0100    0.0001
   380        0.8774             nan     0.0100    0.0000
   400        0.8718             nan     0.0100    0.0000
   420        0.8668             nan     0.0100    0.0000
   440        0.8619             nan     0.0100    0.0000
   460        0.8575             nan     0.0100    0.0000
   480        0.8534             nan     0.0100   -0.0000
   500        0.8496             nan     0.0100   -0.0001
   520        0.8461             nan     0.0100   -0.0001
   540        0.8429             nan     0.0100    0.0000
   560        0.8396             nan     0.0100    0.0000
   580        0.8365             nan     0.0100    0.0000
   600        0.8335             nan     0.0100    0.0000
   620        0.8306             nan     0.0100    0.0000
   640        0.8279             nan     0.0100    0.0000
   660        0.8253             nan     0.0100   -0.0000
   680        0.8228             nan     0.0100   -0.0000
   700        0.8204             nan     0.0100   -0.0000
   720        0.8180             nan     0.0100    0.0000
   740        0.8157             nan     0.0100    0.0000
   760        0.8137             nan     0.0100   -0.0000
   780        0.8114             nan     0.0100    0.0000
   800        0.8097             nan     0.0100   -0.0000
   820        0.8077             nan     0.0100   -0.0001
   840        0.8059             nan     0.0100   -0.0000
   860        0.8041             nan     0.0100   -0.0000
   880        0.8021             nan     0.0100   -0.0001
   900        0.8004             nan     0.0100    0.0000
   920        0.7988             nan     0.0100   -0.0001
   940        0.7974             nan     0.0100   -0.0000
   960        0.7958             nan     0.0100   -0.0000
   980        0.7946             nan     0.0100   -0.0001
  1000        0.7932             nan     0.0100   -0.0000
  1020        0.7917             nan     0.0100   -0.0000
  1040        0.7903             nan     0.0100   -0.0000
  1060        0.7888             nan     0.0100   -0.0000
  1080        0.7873             nan     0.0100    0.0000
  1100        0.7862             nan     0.0100   -0.0001

- Fold09.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0037
     2        1.3165             nan     0.0100    0.0037
     3        1.3092             nan     0.0100    0.0035
     4        1.3019             nan     0.0100    0.0033
     5        1.2943             nan     0.0100    0.0035
     6        1.2875             nan     0.0100    0.0034
     7        1.2806             nan     0.0100    0.0034
     8        1.2739             nan     0.0100    0.0031
     9        1.2674             nan     0.0100    0.0031
    10        1.2608             nan     0.0100    0.0031
    20        1.2022             nan     0.0100    0.0026
    40        1.1118             nan     0.0100    0.0017
    60        1.0468             nan     0.0100    0.0014
    80        0.9989             nan     0.0100    0.0009
   100        0.9638             nan     0.0100    0.0005
   120        0.9343             nan     0.0100    0.0005
   140        0.9103             nan     0.0100    0.0004
   160        0.8913             nan     0.0100    0.0002
   180        0.8758             nan     0.0100    0.0003
   200        0.8629             nan     0.0100    0.0001
   220        0.8513             nan     0.0100   -0.0000
   240        0.8415             nan     0.0100    0.0000
   260        0.8319             nan     0.0100    0.0001
   280        0.8240             nan     0.0100   -0.0000
   300        0.8170             nan     0.0100   -0.0001
   320        0.8103             nan     0.0100    0.0001
   340        0.8038             nan     0.0100   -0.0000
   360        0.7982             nan     0.0100    0.0001
   380        0.7925             nan     0.0100    0.0000
   400        0.7876             nan     0.0100   -0.0000
   420        0.7837             nan     0.0100   -0.0001
   440        0.7793             nan     0.0100    0.0000
   460        0.7751             nan     0.0100   -0.0001
   480        0.7716             nan     0.0100   -0.0001
   500        0.7679             nan     0.0100   -0.0001
   520        0.7648             nan     0.0100   -0.0001
   540        0.7614             nan     0.0100   -0.0000
   560        0.7584             nan     0.0100   -0.0001
   580        0.7552             nan     0.0100   -0.0001
   600        0.7527             nan     0.0100   -0.0001
   620        0.7497             nan     0.0100   -0.0000
   640        0.7470             nan     0.0100   -0.0001
   660        0.7443             nan     0.0100   -0.0001
   680        0.7417             nan     0.0100   -0.0000
   700        0.7395             nan     0.0100   -0.0002
   720        0.7372             nan     0.0100   -0.0001
   740        0.7350             nan     0.0100   -0.0000
   760        0.7330             nan     0.0100   -0.0001
   780        0.7309             nan     0.0100   -0.0001
   800        0.7289             nan     0.0100   -0.0001
   820        0.7266             nan     0.0100   -0.0001
   840        0.7249             nan     0.0100   -0.0001
   860        0.7222             nan     0.0100   -0.0001
   880        0.7206             nan     0.0100   -0.0001
   900        0.7185             nan     0.0100   -0.0000
   920        0.7163             nan     0.0100   -0.0001
   940        0.7144             nan     0.0100   -0.0001
   960        0.7125             nan     0.0100   -0.0002
   980        0.7108             nan     0.0100   -0.0000
  1000        0.7089             nan     0.0100   -0.0001
  1020        0.7071             nan     0.0100   -0.0001
  1040        0.7051             nan     0.0100   -0.0001
  1060        0.7033             nan     0.0100   -0.0000
  1080        0.7017             nan     0.0100   -0.0001
  1100        0.6998             nan     0.0100    0.0000

- Fold09.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3238             nan     0.0100    0.0039
     2        1.3159             nan     0.0100    0.0038
     3        1.3081             nan     0.0100    0.0037
     4        1.3002             nan     0.0100    0.0038
     5        1.2922             nan     0.0100    0.0039
     6        1.2847             nan     0.0100    0.0039
     7        1.2773             nan     0.0100    0.0038
     8        1.2698             nan     0.0100    0.0036
     9        1.2628             nan     0.0100    0.0034
    10        1.2558             nan     0.0100    0.0036
    20        1.1932             nan     0.0100    0.0028
    40        1.0933             nan     0.0100    0.0018
    60        1.0209             nan     0.0100    0.0013
    80        0.9677             nan     0.0100    0.0011
   100        0.9281             nan     0.0100    0.0007
   120        0.8975             nan     0.0100    0.0003
   140        0.8729             nan     0.0100    0.0005
   160        0.8534             nan     0.0100    0.0001
   180        0.8375             nan     0.0100    0.0000
   200        0.8239             nan     0.0100    0.0002
   220        0.8124             nan     0.0100    0.0002
   240        0.8015             nan     0.0100    0.0001
   260        0.7922             nan     0.0100    0.0000
   280        0.7840             nan     0.0100    0.0000
   300        0.7764             nan     0.0100    0.0000
   320        0.7697             nan     0.0100    0.0000
   340        0.7630             nan     0.0100   -0.0000
   360        0.7573             nan     0.0100    0.0001
   380        0.7516             nan     0.0100   -0.0000
   400        0.7468             nan     0.0100   -0.0001
   420        0.7424             nan     0.0100   -0.0001
   440        0.7375             nan     0.0100   -0.0001
   460        0.7334             nan     0.0100   -0.0001
   480        0.7292             nan     0.0100   -0.0001
   500        0.7251             nan     0.0100   -0.0000
   520        0.7217             nan     0.0100   -0.0001
   540        0.7180             nan     0.0100   -0.0001
   560        0.7148             nan     0.0100   -0.0001
   580        0.7111             nan     0.0100   -0.0002
   600        0.7077             nan     0.0100   -0.0001
   620        0.7044             nan     0.0100   -0.0001
   640        0.7006             nan     0.0100   -0.0000
   660        0.6978             nan     0.0100   -0.0001
   680        0.6943             nan     0.0100   -0.0001
   700        0.6908             nan     0.0100   -0.0000
   720        0.6877             nan     0.0100   -0.0002
   740        0.6854             nan     0.0100   -0.0002
   760        0.6827             nan     0.0100   -0.0000
   780        0.6795             nan     0.0100   -0.0001
   800        0.6765             nan     0.0100   -0.0001
   820        0.6733             nan     0.0100   -0.0001
   840        0.6700             nan     0.0100   -0.0002
   860        0.6672             nan     0.0100   -0.0001
   880        0.6642             nan     0.0100   -0.0001
   900        0.6616             nan     0.0100   -0.0002
   920        0.6589             nan     0.0100   -0.0001
   940        0.6565             nan     0.0100   -0.0001
   960        0.6538             nan     0.0100   -0.0001
   980        0.6511             nan     0.0100   -0.0001
  1000        0.6486             nan     0.0100   -0.0000
  1020        0.6464             nan     0.0100   -0.0001
  1040        0.6442             nan     0.0100   -0.0001
  1060        0.6416             nan     0.0100   -0.0001
  1080        0.6393             nan     0.0100   -0.0001
  1100        0.6369             nan     0.0100   -0.0001

- Fold09.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2676             nan     0.1000    0.0274
     2        1.2192             nan     0.1000    0.0238
     3        1.1824             nan     0.1000    0.0180
     4        1.1511             nan     0.1000    0.0162
     5        1.1208             nan     0.1000    0.0131
     6        1.1013             nan     0.1000    0.0108
     7        1.0820             nan     0.1000    0.0090
     8        1.0655             nan     0.1000    0.0067
     9        1.0478             nan     0.1000    0.0076
    10        1.0349             nan     0.1000    0.0060
    20        0.9531             nan     0.1000    0.0039
    40        0.8725             nan     0.1000    0.0013
    60        0.8341             nan     0.1000   -0.0001
    80        0.8083             nan     0.1000   -0.0007
   100        0.7910             nan     0.1000   -0.0006
   120        0.7787             nan     0.1000   -0.0006
   140        0.7690             nan     0.1000   -0.0004
   160        0.7619             nan     0.1000   -0.0003
   180        0.7557             nan     0.1000   -0.0003
   200        0.7497             nan     0.1000   -0.0001
   220        0.7442             nan     0.1000   -0.0015
   240        0.7381             nan     0.1000   -0.0009
   260        0.7336             nan     0.1000   -0.0002
   280        0.7290             nan     0.1000   -0.0008
   300        0.7241             nan     0.1000   -0.0004
   320        0.7219             nan     0.1000   -0.0006
   340        0.7193             nan     0.1000   -0.0006
   360        0.7162             nan     0.1000   -0.0010
   380        0.7122             nan     0.1000   -0.0007
   400        0.7092             nan     0.1000   -0.0010
   420        0.7060             nan     0.1000   -0.0003
   440        0.7033             nan     0.1000   -0.0007
   460        0.6997             nan     0.1000   -0.0009
   480        0.6971             nan     0.1000   -0.0005
   500        0.6943             nan     0.1000   -0.0014
   520        0.6921             nan     0.1000   -0.0012
   540        0.6895             nan     0.1000   -0.0003
   560        0.6884             nan     0.1000   -0.0006
   580        0.6851             nan     0.1000   -0.0003
   600        0.6839             nan     0.1000   -0.0017
   620        0.6823             nan     0.1000   -0.0007
   640        0.6792             nan     0.1000   -0.0009
   660        0.6776             nan     0.1000   -0.0016
   680        0.6762             nan     0.1000   -0.0004
   700        0.6733             nan     0.1000   -0.0012
   720        0.6716             nan     0.1000   -0.0009
   740        0.6704             nan     0.1000   -0.0002
   760        0.6683             nan     0.1000   -0.0013
   780        0.6662             nan     0.1000   -0.0012
   800        0.6635             nan     0.1000   -0.0007
   820        0.6615             nan     0.1000   -0.0012
   840        0.6611             nan     0.1000   -0.0010
   860        0.6593             nan     0.1000   -0.0011
   880        0.6573             nan     0.1000   -0.0005
   900        0.6556             nan     0.1000   -0.0013
   920        0.6537             nan     0.1000   -0.0007
   940        0.6523             nan     0.1000   -0.0009
   960        0.6511             nan     0.1000   -0.0006
   980        0.6503             nan     0.1000   -0.0005
  1000        0.6490             nan     0.1000   -0.0004
  1020        0.6464             nan     0.1000   -0.0008
  1040        0.6463             nan     0.1000   -0.0018
  1060        0.6448             nan     0.1000   -0.0016
  1080        0.6423             nan     0.1000   -0.0006
  1100        0.6401             nan     0.1000   -0.0002

- Fold09.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2551             nan     0.1000    0.0347
     2        1.2004             nan     0.1000    0.0289
     3        1.1511             nan     0.1000    0.0236
     4        1.1083             nan     0.1000    0.0214
     5        1.0709             nan     0.1000    0.0169
     6        1.0404             nan     0.1000    0.0140
     7        1.0149             nan     0.1000    0.0122
     8        0.9944             nan     0.1000    0.0114
     9        0.9758             nan     0.1000    0.0083
    10        0.9597             nan     0.1000    0.0061
    20        0.8651             nan     0.1000    0.0018
    40        0.7845             nan     0.1000    0.0006
    60        0.7492             nan     0.1000   -0.0014
    80        0.7263             nan     0.1000   -0.0014
   100        0.7047             nan     0.1000   -0.0009
   120        0.6861             nan     0.1000   -0.0015
   140        0.6705             nan     0.1000   -0.0010
   160        0.6583             nan     0.1000   -0.0010
   180        0.6410             nan     0.1000   -0.0002
   200        0.6307             nan     0.1000   -0.0010
   220        0.6199             nan     0.1000   -0.0007
   240        0.6094             nan     0.1000   -0.0009
   260        0.6008             nan     0.1000   -0.0012
   280        0.5916             nan     0.1000   -0.0009
   300        0.5824             nan     0.1000   -0.0011
   320        0.5759             nan     0.1000   -0.0015
   340        0.5688             nan     0.1000   -0.0011
   360        0.5622             nan     0.1000   -0.0016
   380        0.5543             nan     0.1000   -0.0017
   400        0.5466             nan     0.1000   -0.0008
   420        0.5392             nan     0.1000   -0.0009
   440        0.5347             nan     0.1000   -0.0018
   460        0.5274             nan     0.1000   -0.0011
   480        0.5183             nan     0.1000   -0.0008
   500        0.5152             nan     0.1000   -0.0004
   520        0.5090             nan     0.1000   -0.0004
   540        0.5030             nan     0.1000   -0.0013
   560        0.4981             nan     0.1000   -0.0014
   580        0.4909             nan     0.1000   -0.0007
   600        0.4862             nan     0.1000   -0.0010
   620        0.4820             nan     0.1000   -0.0009
   640        0.4782             nan     0.1000   -0.0008
   660        0.4742             nan     0.1000   -0.0006
   680        0.4701             nan     0.1000   -0.0008
   700        0.4650             nan     0.1000   -0.0009
   720        0.4605             nan     0.1000   -0.0006
   740        0.4551             nan     0.1000   -0.0013
   760        0.4486             nan     0.1000   -0.0015
   780        0.4429             nan     0.1000   -0.0012
   800        0.4390             nan     0.1000   -0.0011
   820        0.4340             nan     0.1000   -0.0010
   840        0.4312             nan     0.1000   -0.0020
   860        0.4283             nan     0.1000   -0.0003
   880        0.4248             nan     0.1000   -0.0008
   900        0.4217             nan     0.1000   -0.0009
   920        0.4164             nan     0.1000   -0.0004
   940        0.4119             nan     0.1000   -0.0007
   960        0.4085             nan     0.1000   -0.0011
   980        0.4066             nan     0.1000   -0.0006
  1000        0.4026             nan     0.1000   -0.0006
  1020        0.4004             nan     0.1000   -0.0010
  1040        0.3975             nan     0.1000   -0.0011
  1060        0.3943             nan     0.1000   -0.0003
  1080        0.3920             nan     0.1000   -0.0008
  1100        0.3896             nan     0.1000   -0.0007

- Fold09.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2576             nan     0.1000    0.0367
     2        1.1939             nan     0.1000    0.0325
     3        1.1386             nan     0.1000    0.0255
     4        1.0925             nan     0.1000    0.0231
     5        1.0539             nan     0.1000    0.0191
     6        1.0224             nan     0.1000    0.0163
     7        0.9950             nan     0.1000    0.0129
     8        0.9703             nan     0.1000    0.0104
     9        0.9477             nan     0.1000    0.0095
    10        0.9306             nan     0.1000    0.0075
    20        0.8244             nan     0.1000    0.0030
    40        0.7470             nan     0.1000   -0.0002
    60        0.7070             nan     0.1000   -0.0007
    80        0.6790             nan     0.1000   -0.0009
   100        0.6525             nan     0.1000   -0.0013
   120        0.6286             nan     0.1000   -0.0019
   140        0.6115             nan     0.1000   -0.0022
   160        0.5935             nan     0.1000   -0.0004
   180        0.5760             nan     0.1000   -0.0025
   200        0.5611             nan     0.1000   -0.0006
   220        0.5444             nan     0.1000   -0.0014
   240        0.5333             nan     0.1000   -0.0009
   260        0.5209             nan     0.1000   -0.0006
   280        0.5085             nan     0.1000   -0.0011
   300        0.4977             nan     0.1000   -0.0009
   320        0.4858             nan     0.1000   -0.0011
   340        0.4773             nan     0.1000   -0.0016
   360        0.4677             nan     0.1000   -0.0019
   380        0.4570             nan     0.1000   -0.0004
   400        0.4513             nan     0.1000   -0.0014
   420        0.4443             nan     0.1000   -0.0007
   440        0.4338             nan     0.1000   -0.0010
   460        0.4277             nan     0.1000   -0.0007
   480        0.4208             nan     0.1000   -0.0006
   500        0.4132             nan     0.1000   -0.0008
   520        0.4049             nan     0.1000   -0.0008
   540        0.3991             nan     0.1000   -0.0010
   560        0.3913             nan     0.1000   -0.0011
   580        0.3886             nan     0.1000   -0.0011
   600        0.3841             nan     0.1000   -0.0009
   620        0.3801             nan     0.1000   -0.0016
   640        0.3726             nan     0.1000   -0.0007
   660        0.3663             nan     0.1000   -0.0012
   680        0.3615             nan     0.1000   -0.0007
   700        0.3555             nan     0.1000   -0.0014
   720        0.3510             nan     0.1000   -0.0011
   740        0.3464             nan     0.1000   -0.0007
   760        0.3420             nan     0.1000   -0.0018
   780        0.3361             nan     0.1000   -0.0011
   800        0.3307             nan     0.1000   -0.0006
   820        0.3266             nan     0.1000   -0.0010
   840        0.3224             nan     0.1000   -0.0010
   860        0.3177             nan     0.1000   -0.0010
   880        0.3128             nan     0.1000   -0.0011
   900        0.3074             nan     0.1000   -0.0014
   920        0.3029             nan     0.1000   -0.0008
   940        0.2987             nan     0.1000   -0.0015
   960        0.2941             nan     0.1000   -0.0003
   980        0.2900             nan     0.1000   -0.0010
  1000        0.2859             nan     0.1000   -0.0007
  1020        0.2839             nan     0.1000   -0.0013
  1040        0.2804             nan     0.1000   -0.0012
  1060        0.2755             nan     0.1000   -0.0014
  1080        0.2715             nan     0.1000   -0.0005
  1100        0.2689             nan     0.1000   -0.0012

- Fold09.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3255             nan     0.0100    0.0026
     2        1.3198             nan     0.0100    0.0026
     3        1.3141             nan     0.0100    0.0027
     4        1.3086             nan     0.0100    0.0027
     5        1.3031             nan     0.0100    0.0026
     6        1.2982             nan     0.0100    0.0025
     7        1.2933             nan     0.0100    0.0025
     8        1.2882             nan     0.0100    0.0025
     9        1.2829             nan     0.0100    0.0024
    10        1.2784             nan     0.0100    0.0024
    20        1.2364             nan     0.0100    0.0019
    40        1.1723             nan     0.0100    0.0014
    60        1.1258             nan     0.0100    0.0010
    80        1.0895             nan     0.0100    0.0008
   100        1.0590             nan     0.0100    0.0006
   120        1.0342             nan     0.0100    0.0005
   140        1.0134             nan     0.0100    0.0002
   160        0.9960             nan     0.0100    0.0003
   180        0.9806             nan     0.0100    0.0002
   200        0.9673             nan     0.0100    0.0002
   220        0.9555             nan     0.0100    0.0001
   240        0.9454             nan     0.0100    0.0001
   260        0.9360             nan     0.0100    0.0001
   280        0.9273             nan     0.0100    0.0001
   300        0.9195             nan     0.0100   -0.0000
   320        0.9120             nan     0.0100    0.0000
   340        0.9055             nan     0.0100    0.0002
   360        0.8995             nan     0.0100    0.0000
   380        0.8941             nan     0.0100    0.0001
   400        0.8894             nan     0.0100    0.0001
   420        0.8848             nan     0.0100   -0.0000
   440        0.8800             nan     0.0100    0.0001
   460        0.8755             nan     0.0100    0.0001
   480        0.8714             nan     0.0100   -0.0000
   500        0.8671             nan     0.0100    0.0000
   520        0.8632             nan     0.0100   -0.0000
   540        0.8595             nan     0.0100   -0.0000
   560        0.8563             nan     0.0100   -0.0000
   580        0.8531             nan     0.0100   -0.0000
   600        0.8501             nan     0.0100   -0.0001
   620        0.8471             nan     0.0100   -0.0000
   640        0.8440             nan     0.0100   -0.0000
   660        0.8414             nan     0.0100    0.0000
   680        0.8386             nan     0.0100    0.0000
   700        0.8361             nan     0.0100    0.0000
   720        0.8339             nan     0.0100    0.0000
   740        0.8315             nan     0.0100   -0.0000
   760        0.8291             nan     0.0100    0.0000
   780        0.8269             nan     0.0100   -0.0001
   800        0.8246             nan     0.0100   -0.0000
   820        0.8222             nan     0.0100    0.0000
   840        0.8201             nan     0.0100    0.0000
   860        0.8182             nan     0.0100    0.0000
   880        0.8161             nan     0.0100   -0.0000
   900        0.8142             nan     0.0100   -0.0000
   920        0.8123             nan     0.0100    0.0000
   940        0.8108             nan     0.0100    0.0000
   960        0.8090             nan     0.0100   -0.0000
   980        0.8073             nan     0.0100    0.0000
  1000        0.8055             nan     0.0100   -0.0001
  1020        0.8042             nan     0.0100   -0.0000
  1040        0.8027             nan     0.0100    0.0000
  1060        0.8014             nan     0.0100   -0.0001
  1080        0.7997             nan     0.0100   -0.0001
  1100        0.7984             nan     0.0100   -0.0001

- Fold10.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0038
     2        1.3169             nan     0.0100    0.0036
     3        1.3100             nan     0.0100    0.0035
     4        1.3032             nan     0.0100    0.0033
     5        1.2965             nan     0.0100    0.0033
     6        1.2904             nan     0.0100    0.0033
     7        1.2843             nan     0.0100    0.0032
     8        1.2780             nan     0.0100    0.0031
     9        1.2716             nan     0.0100    0.0030
    10        1.2652             nan     0.0100    0.0029
    20        1.2096             nan     0.0100    0.0022
    40        1.1249             nan     0.0100    0.0016
    60        1.0619             nan     0.0100    0.0012
    80        1.0159             nan     0.0100    0.0009
   100        0.9803             nan     0.0100    0.0007
   120        0.9510             nan     0.0100    0.0004
   140        0.9289             nan     0.0100    0.0001
   160        0.9104             nan     0.0100    0.0003
   180        0.8952             nan     0.0100    0.0001
   200        0.8818             nan     0.0100    0.0001
   220        0.8696             nan     0.0100    0.0001
   240        0.8599             nan     0.0100    0.0001
   260        0.8505             nan     0.0100    0.0000
   280        0.8425             nan     0.0100    0.0001
   300        0.8343             nan     0.0100    0.0001
   320        0.8279             nan     0.0100    0.0000
   340        0.8210             nan     0.0100    0.0000
   360        0.8147             nan     0.0100    0.0002
   380        0.8091             nan     0.0100    0.0000
   400        0.8035             nan     0.0100   -0.0000
   420        0.7981             nan     0.0100    0.0001
   440        0.7935             nan     0.0100   -0.0001
   460        0.7894             nan     0.0100   -0.0001
   480        0.7849             nan     0.0100   -0.0001
   500        0.7806             nan     0.0100   -0.0002
   520        0.7770             nan     0.0100   -0.0001
   540        0.7733             nan     0.0100   -0.0000
   560        0.7695             nan     0.0100   -0.0000
   580        0.7660             nan     0.0100   -0.0001
   600        0.7628             nan     0.0100   -0.0001
   620        0.7598             nan     0.0100   -0.0000
   640        0.7569             nan     0.0100    0.0000
   660        0.7539             nan     0.0100   -0.0001
   680        0.7512             nan     0.0100    0.0000
   700        0.7484             nan     0.0100   -0.0000
   720        0.7456             nan     0.0100   -0.0000
   740        0.7430             nan     0.0100   -0.0001
   760        0.7404             nan     0.0100   -0.0001
   780        0.7380             nan     0.0100    0.0000
   800        0.7357             nan     0.0100   -0.0001
   820        0.7337             nan     0.0100   -0.0001
   840        0.7314             nan     0.0100   -0.0001
   860        0.7293             nan     0.0100   -0.0001
   880        0.7273             nan     0.0100   -0.0000
   900        0.7250             nan     0.0100   -0.0000
   920        0.7225             nan     0.0100   -0.0000
   940        0.7205             nan     0.0100   -0.0001
   960        0.7186             nan     0.0100   -0.0001
   980        0.7169             nan     0.0100   -0.0001
  1000        0.7147             nan     0.0100   -0.0001
  1020        0.7126             nan     0.0100   -0.0001
  1040        0.7106             nan     0.0100   -0.0001
  1060        0.7086             nan     0.0100   -0.0000
  1080        0.7070             nan     0.0100    0.0000
  1100        0.7050             nan     0.0100   -0.0001

- Fold10.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3232             nan     0.0100    0.0038
     2        1.3148             nan     0.0100    0.0040
     3        1.3072             nan     0.0100    0.0037
     4        1.2994             nan     0.0100    0.0038
     5        1.2923             nan     0.0100    0.0036
     6        1.2852             nan     0.0100    0.0035
     7        1.2780             nan     0.0100    0.0032
     8        1.2707             nan     0.0100    0.0031
     9        1.2643             nan     0.0100    0.0034
    10        1.2580             nan     0.0100    0.0031
    20        1.1970             nan     0.0100    0.0026
    40        1.1027             nan     0.0100    0.0020
    60        1.0347             nan     0.0100    0.0012
    80        0.9835             nan     0.0100    0.0009
   100        0.9452             nan     0.0100    0.0007
   120        0.9161             nan     0.0100    0.0007
   140        0.8931             nan     0.0100    0.0004
   160        0.8739             nan     0.0100    0.0002
   180        0.8573             nan     0.0100    0.0002
   200        0.8416             nan     0.0100    0.0002
   220        0.8293             nan     0.0100   -0.0001
   240        0.8180             nan     0.0100    0.0000
   260        0.8085             nan     0.0100    0.0001
   280        0.7997             nan     0.0100    0.0001
   300        0.7910             nan     0.0100    0.0001
   320        0.7840             nan     0.0100    0.0001
   340        0.7767             nan     0.0100    0.0000
   360        0.7695             nan     0.0100   -0.0000
   380        0.7631             nan     0.0100   -0.0000
   400        0.7572             nan     0.0100   -0.0001
   420        0.7524             nan     0.0100    0.0000
   440        0.7470             nan     0.0100   -0.0000
   460        0.7418             nan     0.0100   -0.0002
   480        0.7371             nan     0.0100   -0.0002
   500        0.7329             nan     0.0100   -0.0001
   520        0.7284             nan     0.0100   -0.0001
   540        0.7241             nan     0.0100   -0.0001
   560        0.7203             nan     0.0100   -0.0000
   580        0.7165             nan     0.0100   -0.0001
   600        0.7130             nan     0.0100   -0.0000
   620        0.7095             nan     0.0100   -0.0000
   640        0.7065             nan     0.0100   -0.0001
   660        0.7027             nan     0.0100   -0.0000
   680        0.6990             nan     0.0100   -0.0000
   700        0.6958             nan     0.0100    0.0000
   720        0.6924             nan     0.0100   -0.0002
   740        0.6895             nan     0.0100   -0.0002
   760        0.6868             nan     0.0100   -0.0001
   780        0.6835             nan     0.0100   -0.0001
   800        0.6805             nan     0.0100   -0.0001
   820        0.6772             nan     0.0100   -0.0001
   840        0.6748             nan     0.0100   -0.0001
   860        0.6721             nan     0.0100   -0.0001
   880        0.6694             nan     0.0100   -0.0001
   900        0.6665             nan     0.0100   -0.0001
   920        0.6631             nan     0.0100   -0.0001
   940        0.6607             nan     0.0100   -0.0003
   960        0.6579             nan     0.0100   -0.0002
   980        0.6552             nan     0.0100   -0.0002
  1000        0.6527             nan     0.0100   -0.0000
  1020        0.6503             nan     0.0100   -0.0000
  1040        0.6477             nan     0.0100   -0.0001
  1060        0.6448             nan     0.0100   -0.0001
  1080        0.6427             nan     0.0100   -0.0002
  1100        0.6397             nan     0.0100   -0.0002

- Fold10.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2751             nan     0.1000    0.0271
     2        1.2310             nan     0.1000    0.0214
     3        1.1965             nan     0.1000    0.0177
     4        1.1645             nan     0.1000    0.0147
     5        1.1412             nan     0.1000    0.0125
     6        1.1190             nan     0.1000    0.0098
     7        1.1028             nan     0.1000    0.0085
     8        1.0846             nan     0.1000    0.0084
     9        1.0691             nan     0.1000    0.0066
    10        1.0557             nan     0.1000    0.0065
    20        0.9679             nan     0.1000    0.0022
    40        0.8891             nan     0.1000    0.0003
    60        0.8470             nan     0.1000   -0.0002
    80        0.8208             nan     0.1000   -0.0002
   100        0.8039             nan     0.1000   -0.0012
   120        0.7884             nan     0.1000    0.0002
   140        0.7783             nan     0.1000   -0.0008
   160        0.7692             nan     0.1000   -0.0004
   180        0.7618             nan     0.1000   -0.0004
   200        0.7550             nan     0.1000   -0.0005
   220        0.7500             nan     0.1000   -0.0009
   240        0.7459             nan     0.1000   -0.0007
   260        0.7405             nan     0.1000   -0.0007
   280        0.7364             nan     0.1000   -0.0009
   300        0.7329             nan     0.1000   -0.0005
   320        0.7281             nan     0.1000   -0.0004
   340        0.7251             nan     0.1000   -0.0012
   360        0.7220             nan     0.1000   -0.0008
   380        0.7203             nan     0.1000   -0.0008
   400        0.7168             nan     0.1000   -0.0006
   420        0.7128             nan     0.1000   -0.0004
   440        0.7107             nan     0.1000   -0.0013
   460        0.7090             nan     0.1000   -0.0021
   480        0.7056             nan     0.1000   -0.0012
   500        0.7010             nan     0.1000   -0.0001
   520        0.6982             nan     0.1000   -0.0009
   540        0.6948             nan     0.1000   -0.0005
   560        0.6926             nan     0.1000   -0.0005
   580        0.6905             nan     0.1000   -0.0015
   600        0.6879             nan     0.1000   -0.0006
   620        0.6867             nan     0.1000   -0.0012
   640        0.6850             nan     0.1000   -0.0016
   660        0.6819             nan     0.1000   -0.0008
   680        0.6800             nan     0.1000   -0.0005
   700        0.6782             nan     0.1000   -0.0011
   720        0.6769             nan     0.1000   -0.0004
   740        0.6758             nan     0.1000   -0.0004
   760        0.6743             nan     0.1000   -0.0002
   780        0.6716             nan     0.1000   -0.0006
   800        0.6711             nan     0.1000   -0.0009
   820        0.6700             nan     0.1000   -0.0006
   840        0.6683             nan     0.1000   -0.0022
   860        0.6663             nan     0.1000   -0.0021
   880        0.6655             nan     0.1000   -0.0006
   900        0.6646             nan     0.1000   -0.0007
   920        0.6617             nan     0.1000   -0.0007
   940        0.6600             nan     0.1000   -0.0009
   960        0.6588             nan     0.1000   -0.0015
   980        0.6563             nan     0.1000   -0.0006
  1000        0.6556             nan     0.1000   -0.0014
  1020        0.6527             nan     0.1000   -0.0009
  1040        0.6510             nan     0.1000   -0.0006
  1060        0.6490             nan     0.1000   -0.0009
  1080        0.6474             nan     0.1000   -0.0008
  1100        0.6457             nan     0.1000   -0.0004

- Fold10.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2617             nan     0.1000    0.0313
     2        1.2039             nan     0.1000    0.0276
     3        1.1585             nan     0.1000    0.0229
     4        1.1201             nan     0.1000    0.0198
     5        1.0859             nan     0.1000    0.0160
     6        1.0581             nan     0.1000    0.0124
     7        1.0358             nan     0.1000    0.0108
     8        1.0169             nan     0.1000    0.0074
     9        0.9955             nan     0.1000    0.0091
    10        0.9781             nan     0.1000    0.0075
    20        0.8837             nan     0.1000    0.0003
    40        0.8023             nan     0.1000    0.0012
    60        0.7714             nan     0.1000   -0.0008
    80        0.7459             nan     0.1000   -0.0006
   100        0.7192             nan     0.1000   -0.0002
   120        0.6992             nan     0.1000   -0.0007
   140        0.6853             nan     0.1000   -0.0017
   160        0.6724             nan     0.1000   -0.0017
   180        0.6565             nan     0.1000   -0.0016
   200        0.6432             nan     0.1000   -0.0016
   220        0.6346             nan     0.1000   -0.0017
   240        0.6259             nan     0.1000   -0.0013
   260        0.6181             nan     0.1000   -0.0012
   280        0.6061             nan     0.1000   -0.0004
   300        0.5964             nan     0.1000   -0.0015
   320        0.5892             nan     0.1000   -0.0006
   340        0.5793             nan     0.1000   -0.0013
   360        0.5712             nan     0.1000   -0.0012
   380        0.5662             nan     0.1000   -0.0018
   400        0.5576             nan     0.1000   -0.0009
   420        0.5512             nan     0.1000   -0.0006
   440        0.5413             nan     0.1000   -0.0011
   460        0.5355             nan     0.1000   -0.0008
   480        0.5290             nan     0.1000   -0.0017
   500        0.5206             nan     0.1000   -0.0011
   520        0.5135             nan     0.1000   -0.0006
   540        0.5079             nan     0.1000   -0.0011
   560        0.5033             nan     0.1000   -0.0010
   580        0.4999             nan     0.1000   -0.0015
   600        0.4945             nan     0.1000   -0.0008
   620        0.4887             nan     0.1000   -0.0007
   640        0.4824             nan     0.1000   -0.0005
   660        0.4770             nan     0.1000   -0.0013
   680        0.4710             nan     0.1000   -0.0006
   700        0.4649             nan     0.1000   -0.0016
   720        0.4604             nan     0.1000   -0.0012
   740        0.4568             nan     0.1000   -0.0009
   760        0.4519             nan     0.1000   -0.0009
   780        0.4472             nan     0.1000   -0.0006
   800        0.4430             nan     0.1000   -0.0007
   820        0.4372             nan     0.1000   -0.0006
   840        0.4313             nan     0.1000   -0.0007
   860        0.4284             nan     0.1000   -0.0007
   880        0.4259             nan     0.1000   -0.0006
   900        0.4235             nan     0.1000   -0.0006
   920        0.4201             nan     0.1000   -0.0007
   940        0.4154             nan     0.1000   -0.0008
   960        0.4116             nan     0.1000   -0.0009
   980        0.4074             nan     0.1000   -0.0006
  1000        0.4039             nan     0.1000   -0.0009
  1020        0.3994             nan     0.1000   -0.0012
  1040        0.3954             nan     0.1000   -0.0010
  1060        0.3932             nan     0.1000   -0.0007
  1080        0.3910             nan     0.1000   -0.0009
  1100        0.3891             nan     0.1000   -0.0013

- Fold10.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2562             nan     0.1000    0.0363
     2        1.1954             nan     0.1000    0.0293
     3        1.1489             nan     0.1000    0.0206
     4        1.1056             nan     0.1000    0.0217
     5        1.0692             nan     0.1000    0.0184
     6        1.0369             nan     0.1000    0.0150
     7        1.0082             nan     0.1000    0.0127
     8        0.9862             nan     0.1000    0.0098
     9        0.9707             nan     0.1000    0.0076
    10        0.9531             nan     0.1000    0.0057
    20        0.8454             nan     0.1000   -0.0003
    40        0.7668             nan     0.1000   -0.0007
    60        0.7203             nan     0.1000   -0.0014
    80        0.6886             nan     0.1000   -0.0007
   100        0.6627             nan     0.1000   -0.0005
   120        0.6416             nan     0.1000   -0.0003
   140        0.6216             nan     0.1000   -0.0017
   160        0.6033             nan     0.1000   -0.0013
   180        0.5785             nan     0.1000   -0.0008
   200        0.5646             nan     0.1000   -0.0007
   220        0.5456             nan     0.1000   -0.0011
   240        0.5301             nan     0.1000   -0.0005
   260        0.5197             nan     0.1000   -0.0008
   280        0.5092             nan     0.1000   -0.0011
   300        0.4961             nan     0.1000   -0.0009
   320        0.4844             nan     0.1000   -0.0022
   340        0.4745             nan     0.1000   -0.0016
   360        0.4627             nan     0.1000   -0.0010
   380        0.4543             nan     0.1000   -0.0021
   400        0.4433             nan     0.1000   -0.0007
   420        0.4349             nan     0.1000   -0.0019
   440        0.4282             nan     0.1000   -0.0015
   460        0.4210             nan     0.1000   -0.0007
   480        0.4142             nan     0.1000   -0.0015
   500        0.4058             nan     0.1000   -0.0015
   520        0.3963             nan     0.1000   -0.0009
   540        0.3901             nan     0.1000   -0.0012
   560        0.3827             nan     0.1000   -0.0005
   580        0.3759             nan     0.1000   -0.0007
   600        0.3678             nan     0.1000   -0.0001
   620        0.3619             nan     0.1000   -0.0014
   640        0.3538             nan     0.1000   -0.0010
   660        0.3489             nan     0.1000   -0.0006
   680        0.3449             nan     0.1000   -0.0016
   700        0.3396             nan     0.1000   -0.0011
   720        0.3318             nan     0.1000   -0.0005
   740        0.3271             nan     0.1000   -0.0014
   760        0.3227             nan     0.1000   -0.0012
   780        0.3183             nan     0.1000   -0.0015
   800        0.3137             nan     0.1000   -0.0007
   820        0.3084             nan     0.1000   -0.0008
   840        0.3039             nan     0.1000   -0.0006
   860        0.3008             nan     0.1000   -0.0008
   880        0.2953             nan     0.1000   -0.0006
   900        0.2916             nan     0.1000   -0.0010
   920        0.2877             nan     0.1000   -0.0010
   940        0.2831             nan     0.1000   -0.0004
   960        0.2790             nan     0.1000   -0.0013
   980        0.2758             nan     0.1000   -0.0016
  1000        0.2719             nan     0.1000   -0.0010
  1020        0.2675             nan     0.1000   -0.0008
  1040        0.2638             nan     0.1000   -0.0008
  1060        0.2600             nan     0.1000   -0.0005
  1080        0.2562             nan     0.1000   -0.0010
  1100        0.2538             nan     0.1000   -0.0008

- Fold10.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3247             nan     0.0100    0.0033
     2        1.3180             nan     0.0100    0.0030
     3        1.3118             nan     0.0100    0.0031
     4        1.3056             nan     0.0100    0.0029
     5        1.2995             nan     0.0100    0.0029
     6        1.2937             nan     0.0100    0.0030
     7        1.2879             nan     0.0100    0.0029
     8        1.2821             nan     0.0100    0.0027
     9        1.2767             nan     0.0100    0.0028
    10        1.2712             nan     0.0100    0.0027
    20        1.2209             nan     0.0100    0.0022
    40        1.1446             nan     0.0100    0.0015
    60        1.0920             nan     0.0100    0.0007
    80        1.0542             nan     0.0100    0.0008
   100        1.0234             nan     0.0100    0.0005
   120        0.9984             nan     0.0100    0.0005
   140        0.9775             nan     0.0100    0.0004
   160        0.9589             nan     0.0100    0.0003
   180        0.9446             nan     0.0100    0.0002
   200        0.9308             nan     0.0100    0.0001
   220        0.9192             nan     0.0100    0.0002
   240        0.9087             nan     0.0100    0.0001
   260        0.8993             nan     0.0100    0.0002
   280        0.8912             nan     0.0100    0.0001
   300        0.8834             nan     0.0100    0.0002
   320        0.8764             nan     0.0100    0.0000
   340        0.8699             nan     0.0100    0.0001
   360        0.8645             nan     0.0100    0.0001
   380        0.8593             nan     0.0100   -0.0001
   400        0.8543             nan     0.0100    0.0001
   420        0.8500             nan     0.0100   -0.0001
   440        0.8456             nan     0.0100   -0.0001
   460        0.8416             nan     0.0100    0.0001
   480        0.8376             nan     0.0100    0.0000
   500        0.8339             nan     0.0100    0.0000
   520        0.8304             nan     0.0100   -0.0000
   540        0.8270             nan     0.0100   -0.0002
   560        0.8240             nan     0.0100    0.0000
   580        0.8209             nan     0.0100    0.0000
   600        0.8180             nan     0.0100   -0.0000
   620        0.8152             nan     0.0100   -0.0000
   640        0.8127             nan     0.0100    0.0000
   660        0.8102             nan     0.0100    0.0000
   680        0.8079             nan     0.0100   -0.0001
   700        0.8058             nan     0.0100    0.0000
   720        0.8038             nan     0.0100   -0.0001
   740        0.8019             nan     0.0100    0.0000
   760        0.7998             nan     0.0100   -0.0000
   780        0.7981             nan     0.0100   -0.0000
   800        0.7963             nan     0.0100   -0.0000
   820        0.7946             nan     0.0100   -0.0001
   840        0.7928             nan     0.0100   -0.0001
   860        0.7910             nan     0.0100    0.0000
   880        0.7892             nan     0.0100    0.0000
   900        0.7876             nan     0.0100   -0.0000
   920        0.7861             nan     0.0100   -0.0001
   940        0.7846             nan     0.0100   -0.0000
   960        0.7831             nan     0.0100   -0.0000
   980        0.7817             nan     0.0100   -0.0001
  1000        0.7802             nan     0.0100   -0.0000
  1020        0.7790             nan     0.0100   -0.0001
  1040        0.7778             nan     0.0100   -0.0001
  1060        0.7767             nan     0.0100   -0.0001
  1080        0.7756             nan     0.0100   -0.0000
  1100        0.7744             nan     0.0100   -0.0000

- Fold01.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0037
     2        1.3163             nan     0.0100    0.0039
     3        1.3083             nan     0.0100    0.0037
     4        1.3007             nan     0.0100    0.0038
     5        1.2930             nan     0.0100    0.0039
     6        1.2858             nan     0.0100    0.0036
     7        1.2783             nan     0.0100    0.0033
     8        1.2713             nan     0.0100    0.0029
     9        1.2646             nan     0.0100    0.0034
    10        1.2575             nan     0.0100    0.0033
    20        1.1973             nan     0.0100    0.0028
    40        1.1035             nan     0.0100    0.0020
    60        1.0357             nan     0.0100    0.0014
    80        0.9858             nan     0.0100    0.0010
   100        0.9471             nan     0.0100    0.0007
   120        0.9170             nan     0.0100    0.0006
   140        0.8925             nan     0.0100    0.0004
   160        0.8749             nan     0.0100    0.0003
   180        0.8599             nan     0.0100    0.0002
   200        0.8474             nan     0.0100    0.0002
   220        0.8360             nan     0.0100    0.0002
   240        0.8261             nan     0.0100    0.0001
   260        0.8174             nan     0.0100    0.0001
   280        0.8101             nan     0.0100    0.0000
   300        0.8035             nan     0.0100    0.0000
   320        0.7970             nan     0.0100   -0.0000
   340        0.7911             nan     0.0100    0.0001
   360        0.7855             nan     0.0100   -0.0000
   380        0.7798             nan     0.0100    0.0001
   400        0.7744             nan     0.0100    0.0001
   420        0.7700             nan     0.0100    0.0000
   440        0.7657             nan     0.0100   -0.0001
   460        0.7615             nan     0.0100   -0.0000
   480        0.7576             nan     0.0100   -0.0000
   500        0.7542             nan     0.0100    0.0000
   520        0.7510             nan     0.0100   -0.0001
   540        0.7477             nan     0.0100   -0.0001
   560        0.7446             nan     0.0100   -0.0000
   580        0.7411             nan     0.0100   -0.0000
   600        0.7383             nan     0.0100   -0.0000
   620        0.7352             nan     0.0100   -0.0000
   640        0.7324             nan     0.0100    0.0000
   660        0.7299             nan     0.0100   -0.0001
   680        0.7273             nan     0.0100   -0.0000
   700        0.7246             nan     0.0100   -0.0002
   720        0.7221             nan     0.0100   -0.0001
   740        0.7194             nan     0.0100   -0.0002
   760        0.7172             nan     0.0100   -0.0000
   780        0.7150             nan     0.0100   -0.0001
   800        0.7127             nan     0.0100   -0.0002
   820        0.7106             nan     0.0100   -0.0000
   840        0.7088             nan     0.0100   -0.0002
   860        0.7066             nan     0.0100   -0.0001
   880        0.7047             nan     0.0100   -0.0000
   900        0.7027             nan     0.0100   -0.0001
   920        0.7006             nan     0.0100   -0.0002
   940        0.6989             nan     0.0100   -0.0001
   960        0.6967             nan     0.0100   -0.0000
   980        0.6948             nan     0.0100   -0.0001
  1000        0.6928             nan     0.0100   -0.0001
  1020        0.6912             nan     0.0100   -0.0001
  1040        0.6895             nan     0.0100   -0.0001
  1060        0.6879             nan     0.0100   -0.0001
  1080        0.6862             nan     0.0100   -0.0001
  1100        0.6843             nan     0.0100   -0.0001

- Fold01.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3235             nan     0.0100    0.0040
     2        1.3151             nan     0.0100    0.0039
     3        1.3066             nan     0.0100    0.0040
     4        1.2987             nan     0.0100    0.0037
     5        1.2907             nan     0.0100    0.0041
     6        1.2826             nan     0.0100    0.0038
     7        1.2754             nan     0.0100    0.0036
     8        1.2677             nan     0.0100    0.0034
     9        1.2601             nan     0.0100    0.0036
    10        1.2533             nan     0.0100    0.0035
    20        1.1874             nan     0.0100    0.0028
    40        1.0840             nan     0.0100    0.0021
    60        1.0112             nan     0.0100    0.0016
    80        0.9561             nan     0.0100    0.0010
   100        0.9150             nan     0.0100    0.0004
   120        0.8834             nan     0.0100    0.0004
   140        0.8584             nan     0.0100    0.0004
   160        0.8384             nan     0.0100    0.0004
   180        0.8224             nan     0.0100    0.0003
   200        0.8093             nan     0.0100    0.0001
   220        0.7970             nan     0.0100    0.0001
   240        0.7870             nan     0.0100   -0.0001
   260        0.7782             nan     0.0100    0.0002
   280        0.7695             nan     0.0100   -0.0001
   300        0.7618             nan     0.0100    0.0001
   320        0.7548             nan     0.0100    0.0001
   340        0.7480             nan     0.0100   -0.0001
   360        0.7423             nan     0.0100    0.0000
   380        0.7372             nan     0.0100   -0.0000
   400        0.7317             nan     0.0100   -0.0000
   420        0.7266             nan     0.0100   -0.0001
   440        0.7219             nan     0.0100   -0.0001
   460        0.7172             nan     0.0100   -0.0001
   480        0.7128             nan     0.0100   -0.0000
   500        0.7093             nan     0.0100   -0.0000
   520        0.7051             nan     0.0100    0.0000
   540        0.7013             nan     0.0100   -0.0001
   560        0.6981             nan     0.0100   -0.0001
   580        0.6950             nan     0.0100   -0.0001
   600        0.6919             nan     0.0100   -0.0001
   620        0.6882             nan     0.0100   -0.0002
   640        0.6847             nan     0.0100   -0.0000
   660        0.6816             nan     0.0100   -0.0000
   680        0.6784             nan     0.0100   -0.0001
   700        0.6752             nan     0.0100   -0.0001
   720        0.6721             nan     0.0100   -0.0001
   740        0.6694             nan     0.0100    0.0000
   760        0.6659             nan     0.0100   -0.0001
   780        0.6630             nan     0.0100   -0.0002
   800        0.6598             nan     0.0100   -0.0002
   820        0.6572             nan     0.0100   -0.0001
   840        0.6546             nan     0.0100   -0.0003
   860        0.6521             nan     0.0100   -0.0001
   880        0.6492             nan     0.0100   -0.0001
   900        0.6466             nan     0.0100   -0.0001
   920        0.6437             nan     0.0100   -0.0001
   940        0.6413             nan     0.0100   -0.0001
   960        0.6387             nan     0.0100   -0.0001
   980        0.6364             nan     0.0100   -0.0001
  1000        0.6341             nan     0.0100   -0.0001
  1020        0.6314             nan     0.0100   -0.0001
  1040        0.6290             nan     0.0100   -0.0001
  1060        0.6264             nan     0.0100   -0.0001
  1080        0.6241             nan     0.0100   -0.0001
  1100        0.6214             nan     0.0100   -0.0002

- Fold01.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2655             nan     0.1000    0.0317
     2        1.2187             nan     0.1000    0.0245
     3        1.1798             nan     0.1000    0.0206
     4        1.1425             nan     0.1000    0.0173
     5        1.1135             nan     0.1000    0.0142
     6        1.0905             nan     0.1000    0.0115
     7        1.0710             nan     0.1000    0.0094
     8        1.0558             nan     0.1000    0.0064
     9        1.0378             nan     0.1000    0.0072
    10        1.0235             nan     0.1000    0.0060
    20        0.9317             nan     0.1000    0.0012
    40        0.8571             nan     0.1000   -0.0006
    60        0.8199             nan     0.1000    0.0005
    80        0.7966             nan     0.1000   -0.0001
   100        0.7802             nan     0.1000   -0.0011
   120        0.7690             nan     0.1000   -0.0004
   140        0.7619             nan     0.1000   -0.0011
   160        0.7520             nan     0.1000   -0.0004
   180        0.7465             nan     0.1000   -0.0012
   200        0.7404             nan     0.1000   -0.0002
   220        0.7347             nan     0.1000   -0.0012
   240        0.7289             nan     0.1000   -0.0007
   260        0.7249             nan     0.1000   -0.0005
   280        0.7207             nan     0.1000   -0.0011
   300        0.7161             nan     0.1000   -0.0005
   320        0.7131             nan     0.1000   -0.0004
   340        0.7101             nan     0.1000   -0.0010
   360        0.7059             nan     0.1000   -0.0005
   380        0.7025             nan     0.1000   -0.0013
   400        0.6995             nan     0.1000   -0.0007
   420        0.6976             nan     0.1000   -0.0004
   440        0.6947             nan     0.1000   -0.0006
   460        0.6926             nan     0.1000   -0.0006
   480        0.6899             nan     0.1000   -0.0010
   500        0.6884             nan     0.1000   -0.0008
   520        0.6871             nan     0.1000   -0.0013
   540        0.6832             nan     0.1000   -0.0009
   560        0.6814             nan     0.1000   -0.0012
   580        0.6787             nan     0.1000   -0.0009
   600        0.6759             nan     0.1000   -0.0007
   620        0.6758             nan     0.1000   -0.0009
   640        0.6735             nan     0.1000   -0.0007
   660        0.6714             nan     0.1000   -0.0004
   680        0.6703             nan     0.1000   -0.0007
   700        0.6680             nan     0.1000   -0.0013
   720        0.6663             nan     0.1000   -0.0005
   740        0.6639             nan     0.1000   -0.0012
   760        0.6617             nan     0.1000   -0.0006
   780        0.6602             nan     0.1000   -0.0008
   800        0.6599             nan     0.1000   -0.0012
   820        0.6586             nan     0.1000   -0.0009
   840        0.6569             nan     0.1000   -0.0005
   860        0.6559             nan     0.1000   -0.0005
   880        0.6540             nan     0.1000   -0.0004
   900        0.6512             nan     0.1000   -0.0005
   920        0.6513             nan     0.1000   -0.0007
   940        0.6474             nan     0.1000   -0.0015
   960        0.6464             nan     0.1000   -0.0005
   980        0.6457             nan     0.1000   -0.0009
  1000        0.6455             nan     0.1000   -0.0005
  1020        0.6428             nan     0.1000   -0.0013
  1040        0.6407             nan     0.1000   -0.0010
  1060        0.6400             nan     0.1000   -0.0011
  1080        0.6386             nan     0.1000   -0.0007
  1100        0.6381             nan     0.1000   -0.0004

- Fold01.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2587             nan     0.1000    0.0351
     2        1.1947             nan     0.1000    0.0317
     3        1.1428             nan     0.1000    0.0251
     4        1.0988             nan     0.1000    0.0212
     5        1.0635             nan     0.1000    0.0196
     6        1.0321             nan     0.1000    0.0157
     7        1.0080             nan     0.1000    0.0104
     8        0.9850             nan     0.1000    0.0106
     9        0.9670             nan     0.1000    0.0078
    10        0.9465             nan     0.1000    0.0086
    20        0.8423             nan     0.1000    0.0025
    40        0.7784             nan     0.1000   -0.0006
    60        0.7365             nan     0.1000    0.0000
    80        0.7183             nan     0.1000   -0.0005
   100        0.7010             nan     0.1000   -0.0015
   120        0.6843             nan     0.1000   -0.0015
   140        0.6634             nan     0.1000   -0.0003
   160        0.6536             nan     0.1000   -0.0012
   180        0.6405             nan     0.1000   -0.0002
   200        0.6258             nan     0.1000   -0.0018
   220        0.6148             nan     0.1000   -0.0004
   240        0.6062             nan     0.1000   -0.0000
   260        0.5957             nan     0.1000   -0.0010
   280        0.5850             nan     0.1000   -0.0006
   300        0.5751             nan     0.1000   -0.0010
   320        0.5649             nan     0.1000   -0.0007
   340        0.5577             nan     0.1000   -0.0008
   360        0.5497             nan     0.1000   -0.0010
   380        0.5428             nan     0.1000   -0.0015
   400        0.5361             nan     0.1000   -0.0008
   420        0.5286             nan     0.1000   -0.0011
   440        0.5220             nan     0.1000   -0.0008
   460        0.5155             nan     0.1000   -0.0004
   480        0.5091             nan     0.1000   -0.0002
   500        0.5023             nan     0.1000   -0.0010
   520        0.4952             nan     0.1000   -0.0017
   540        0.4899             nan     0.1000   -0.0010
   560        0.4853             nan     0.1000   -0.0015
   580        0.4799             nan     0.1000   -0.0009
   600        0.4758             nan     0.1000   -0.0014
   620        0.4723             nan     0.1000   -0.0011
   640        0.4655             nan     0.1000   -0.0012
   660        0.4601             nan     0.1000   -0.0005
   680        0.4543             nan     0.1000   -0.0003
   700        0.4513             nan     0.1000   -0.0009
   720        0.4481             nan     0.1000   -0.0003
   740        0.4435             nan     0.1000   -0.0012
   760        0.4377             nan     0.1000   -0.0007
   780        0.4341             nan     0.1000   -0.0008
   800        0.4301             nan     0.1000   -0.0009
   820        0.4270             nan     0.1000   -0.0005
   840        0.4243             nan     0.1000   -0.0008
   860        0.4199             nan     0.1000   -0.0013
   880        0.4165             nan     0.1000   -0.0008
   900        0.4121             nan     0.1000   -0.0012
   920        0.4092             nan     0.1000   -0.0009
   940        0.4054             nan     0.1000   -0.0008
   960        0.4023             nan     0.1000   -0.0006
   980        0.3983             nan     0.1000   -0.0013
  1000        0.3969             nan     0.1000   -0.0006
  1020        0.3944             nan     0.1000   -0.0008
  1040        0.3912             nan     0.1000   -0.0008
  1060        0.3891             nan     0.1000   -0.0006
  1080        0.3867             nan     0.1000   -0.0007
  1100        0.3834             nan     0.1000   -0.0006

- Fold01.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2471             nan     0.1000    0.0425
     2        1.1819             nan     0.1000    0.0344
     3        1.1269             nan     0.1000    0.0242
     4        1.0810             nan     0.1000    0.0202
     5        1.0394             nan     0.1000    0.0201
     6        1.0059             nan     0.1000    0.0160
     7        0.9779             nan     0.1000    0.0145
     8        0.9553             nan     0.1000    0.0107
     9        0.9334             nan     0.1000    0.0102
    10        0.9132             nan     0.1000    0.0076
    20        0.8140             nan     0.1000    0.0029
    40        0.7387             nan     0.1000    0.0000
    60        0.6941             nan     0.1000   -0.0011
    80        0.6639             nan     0.1000   -0.0013
   100        0.6393             nan     0.1000   -0.0007
   120        0.6128             nan     0.1000   -0.0013
   140        0.5923             nan     0.1000   -0.0008
   160        0.5728             nan     0.1000   -0.0011
   180        0.5574             nan     0.1000   -0.0015
   200        0.5435             nan     0.1000   -0.0010
   220        0.5294             nan     0.1000   -0.0005
   240        0.5178             nan     0.1000   -0.0011
   260        0.5089             nan     0.1000   -0.0007
   280        0.4972             nan     0.1000   -0.0014
   300        0.4856             nan     0.1000   -0.0009
   320        0.4746             nan     0.1000   -0.0008
   340        0.4651             nan     0.1000   -0.0010
   360        0.4554             nan     0.1000   -0.0008
   380        0.4443             nan     0.1000   -0.0008
   400        0.4365             nan     0.1000   -0.0010
   420        0.4285             nan     0.1000   -0.0009
   440        0.4194             nan     0.1000   -0.0008
   460        0.4118             nan     0.1000   -0.0008
   480        0.4029             nan     0.1000   -0.0020
   500        0.3954             nan     0.1000   -0.0016
   520        0.3900             nan     0.1000   -0.0009
   540        0.3819             nan     0.1000   -0.0009
   560        0.3744             nan     0.1000   -0.0008
   580        0.3686             nan     0.1000   -0.0014
   600        0.3617             nan     0.1000   -0.0005
   620        0.3568             nan     0.1000   -0.0008
   640        0.3510             nan     0.1000   -0.0013
   660        0.3432             nan     0.1000   -0.0004
   680        0.3371             nan     0.1000   -0.0014
   700        0.3334             nan     0.1000   -0.0004
   720        0.3293             nan     0.1000   -0.0011
   740        0.3258             nan     0.1000   -0.0009
   760        0.3214             nan     0.1000   -0.0014
   780        0.3164             nan     0.1000   -0.0009
   800        0.3123             nan     0.1000   -0.0008
   820        0.3092             nan     0.1000   -0.0007
   840        0.3059             nan     0.1000   -0.0004
   860        0.3020             nan     0.1000   -0.0007
   880        0.2970             nan     0.1000   -0.0005
   900        0.2940             nan     0.1000   -0.0006
   920        0.2914             nan     0.1000   -0.0012
   940        0.2874             nan     0.1000   -0.0006
   960        0.2846             nan     0.1000   -0.0014
   980        0.2812             nan     0.1000   -0.0006
  1000        0.2755             nan     0.1000   -0.0007
  1020        0.2727             nan     0.1000   -0.0008
  1040        0.2688             nan     0.1000   -0.0005
  1060        0.2665             nan     0.1000   -0.0016
  1080        0.2649             nan     0.1000   -0.0012
  1100        0.2620             nan     0.1000   -0.0010

- Fold01.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3257             nan     0.0100    0.0031
     2        1.3200             nan     0.0100    0.0029
     3        1.3144             nan     0.0100    0.0029
     4        1.3089             nan     0.0100    0.0029
     5        1.3031             nan     0.0100    0.0028
     6        1.2973             nan     0.0100    0.0026
     7        1.2912             nan     0.0100    0.0024
     8        1.2861             nan     0.0100    0.0026
     9        1.2805             nan     0.0100    0.0025
    10        1.2752             nan     0.0100    0.0024
    20        1.2304             nan     0.0100    0.0021
    40        1.1598             nan     0.0100    0.0014
    60        1.1111             nan     0.0100    0.0010
    80        1.0752             nan     0.0100    0.0008
   100        1.0446             nan     0.0100    0.0006
   120        1.0213             nan     0.0100    0.0005
   140        1.0027             nan     0.0100    0.0003
   160        0.9857             nan     0.0100    0.0002
   180        0.9713             nan     0.0100    0.0003
   200        0.9581             nan     0.0100    0.0002
   220        0.9472             nan     0.0100    0.0001
   240        0.9369             nan     0.0100    0.0002
   260        0.9275             nan     0.0100    0.0001
   280        0.9198             nan     0.0100    0.0000
   300        0.9122             nan     0.0100    0.0001
   320        0.9055             nan     0.0100    0.0001
   340        0.8989             nan     0.0100    0.0001
   360        0.8930             nan     0.0100    0.0001
   380        0.8878             nan     0.0100    0.0000
   400        0.8833             nan     0.0100    0.0000
   420        0.8783             nan     0.0100    0.0001
   440        0.8741             nan     0.0100   -0.0001
   460        0.8701             nan     0.0100    0.0000
   480        0.8663             nan     0.0100    0.0000
   500        0.8620             nan     0.0100   -0.0000
   520        0.8581             nan     0.0100    0.0001
   540        0.8550             nan     0.0100    0.0000
   560        0.8520             nan     0.0100    0.0000
   580        0.8488             nan     0.0100    0.0000
   600        0.8455             nan     0.0100   -0.0000
   620        0.8430             nan     0.0100    0.0000
   640        0.8403             nan     0.0100   -0.0000
   660        0.8375             nan     0.0100   -0.0000
   680        0.8349             nan     0.0100   -0.0000
   700        0.8321             nan     0.0100   -0.0000
   720        0.8295             nan     0.0100    0.0000
   740        0.8271             nan     0.0100   -0.0000
   760        0.8250             nan     0.0100   -0.0001
   780        0.8228             nan     0.0100   -0.0001
   800        0.8207             nan     0.0100   -0.0001
   820        0.8187             nan     0.0100   -0.0000
   840        0.8171             nan     0.0100   -0.0000
   860        0.8152             nan     0.0100   -0.0001
   880        0.8131             nan     0.0100   -0.0000
   900        0.8115             nan     0.0100   -0.0001
   920        0.8098             nan     0.0100   -0.0001
   940        0.8080             nan     0.0100   -0.0000
   960        0.8065             nan     0.0100   -0.0001
   980        0.8050             nan     0.0100    0.0000
  1000        0.8038             nan     0.0100   -0.0000
  1020        0.8024             nan     0.0100   -0.0000
  1040        0.8011             nan     0.0100   -0.0001
  1060        0.7997             nan     0.0100   -0.0002
  1080        0.7985             nan     0.0100   -0.0001
  1100        0.7971             nan     0.0100   -0.0001

- Fold02.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3245             nan     0.0100    0.0038
     2        1.3171             nan     0.0100    0.0035
     3        1.3102             nan     0.0100    0.0036
     4        1.3032             nan     0.0100    0.0035
     5        1.2962             nan     0.0100    0.0035
     6        1.2893             nan     0.0100    0.0031
     7        1.2825             nan     0.0100    0.0032
     8        1.2758             nan     0.0100    0.0030
     9        1.2693             nan     0.0100    0.0032
    10        1.2631             nan     0.0100    0.0031
    20        1.2046             nan     0.0100    0.0026
    40        1.1163             nan     0.0100    0.0017
    60        1.0524             nan     0.0100    0.0012
    80        1.0045             nan     0.0100    0.0009
   100        0.9680             nan     0.0100    0.0007
   120        0.9391             nan     0.0100    0.0006
   140        0.9177             nan     0.0100    0.0004
   160        0.9000             nan     0.0100    0.0002
   180        0.8851             nan     0.0100    0.0002
   200        0.8727             nan     0.0100    0.0002
   220        0.8620             nan     0.0100    0.0002
   240        0.8528             nan     0.0100   -0.0001
   260        0.8433             nan     0.0100    0.0001
   280        0.8344             nan     0.0100    0.0002
   300        0.8268             nan     0.0100    0.0001
   320        0.8194             nan     0.0100   -0.0001
   340        0.8130             nan     0.0100    0.0000
   360        0.8070             nan     0.0100    0.0001
   380        0.8016             nan     0.0100    0.0000
   400        0.7967             nan     0.0100    0.0001
   420        0.7917             nan     0.0100   -0.0001
   440        0.7869             nan     0.0100   -0.0000
   460        0.7825             nan     0.0100   -0.0001
   480        0.7786             nan     0.0100   -0.0000
   500        0.7746             nan     0.0100   -0.0001
   520        0.7712             nan     0.0100   -0.0001
   540        0.7680             nan     0.0100   -0.0001
   560        0.7646             nan     0.0100   -0.0001
   580        0.7616             nan     0.0100   -0.0001
   600        0.7586             nan     0.0100   -0.0001
   620        0.7559             nan     0.0100   -0.0001
   640        0.7528             nan     0.0100    0.0000
   660        0.7504             nan     0.0100   -0.0002
   680        0.7477             nan     0.0100   -0.0001
   700        0.7450             nan     0.0100   -0.0000
   720        0.7427             nan     0.0100   -0.0001
   740        0.7400             nan     0.0100   -0.0001
   760        0.7378             nan     0.0100   -0.0000
   780        0.7356             nan     0.0100   -0.0001
   800        0.7329             nan     0.0100   -0.0000
   820        0.7306             nan     0.0100   -0.0001
   840        0.7284             nan     0.0100   -0.0000
   860        0.7260             nan     0.0100   -0.0001
   880        0.7240             nan     0.0100   -0.0000
   900        0.7217             nan     0.0100    0.0000
   920        0.7198             nan     0.0100   -0.0002
   940        0.7175             nan     0.0100   -0.0001
   960        0.7159             nan     0.0100   -0.0002
   980        0.7138             nan     0.0100   -0.0000
  1000        0.7115             nan     0.0100   -0.0001
  1020        0.7099             nan     0.0100   -0.0001
  1040        0.7081             nan     0.0100   -0.0000
  1060        0.7062             nan     0.0100   -0.0001
  1080        0.7038             nan     0.0100   -0.0001
  1100        0.7019             nan     0.0100   -0.0000

- Fold02.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3239             nan     0.0100    0.0040
     2        1.3158             nan     0.0100    0.0037
     3        1.3077             nan     0.0100    0.0038
     4        1.2992             nan     0.0100    0.0038
     5        1.2915             nan     0.0100    0.0034
     6        1.2833             nan     0.0100    0.0039
     7        1.2763             nan     0.0100    0.0035
     8        1.2689             nan     0.0100    0.0034
     9        1.2616             nan     0.0100    0.0034
    10        1.2548             nan     0.0100    0.0033
    20        1.1916             nan     0.0100    0.0028
    40        1.0962             nan     0.0100    0.0020
    60        1.0269             nan     0.0100    0.0015
    80        0.9742             nan     0.0100    0.0009
   100        0.9350             nan     0.0100    0.0007
   120        0.9040             nan     0.0100    0.0005
   140        0.8811             nan     0.0100    0.0002
   160        0.8625             nan     0.0100    0.0002
   180        0.8460             nan     0.0100    0.0003
   200        0.8320             nan     0.0100    0.0003
   220        0.8192             nan     0.0100    0.0001
   240        0.8087             nan     0.0100    0.0001
   260        0.7994             nan     0.0100   -0.0000
   280        0.7907             nan     0.0100    0.0000
   300        0.7836             nan     0.0100    0.0000
   320        0.7768             nan     0.0100   -0.0001
   340        0.7700             nan     0.0100   -0.0000
   360        0.7633             nan     0.0100   -0.0000
   380        0.7566             nan     0.0100    0.0000
   400        0.7512             nan     0.0100   -0.0000
   420        0.7462             nan     0.0100   -0.0001
   440        0.7419             nan     0.0100   -0.0000
   460        0.7371             nan     0.0100   -0.0002
   480        0.7327             nan     0.0100   -0.0000
   500        0.7289             nan     0.0100   -0.0001
   520        0.7249             nan     0.0100   -0.0001
   540        0.7215             nan     0.0100   -0.0002
   560        0.7168             nan     0.0100    0.0000
   580        0.7134             nan     0.0100   -0.0002
   600        0.7095             nan     0.0100   -0.0001
   620        0.7055             nan     0.0100   -0.0000
   640        0.7016             nan     0.0100   -0.0001
   660        0.6979             nan     0.0100   -0.0001
   680        0.6945             nan     0.0100   -0.0001
   700        0.6913             nan     0.0100   -0.0001
   720        0.6879             nan     0.0100   -0.0001
   740        0.6850             nan     0.0100   -0.0001
   760        0.6820             nan     0.0100   -0.0002
   780        0.6791             nan     0.0100   -0.0001
   800        0.6765             nan     0.0100   -0.0002
   820        0.6736             nan     0.0100   -0.0001
   840        0.6714             nan     0.0100   -0.0002
   860        0.6685             nan     0.0100   -0.0001
   880        0.6656             nan     0.0100   -0.0002
   900        0.6634             nan     0.0100   -0.0000
   920        0.6608             nan     0.0100   -0.0001
   940        0.6583             nan     0.0100   -0.0001
   960        0.6553             nan     0.0100    0.0000
   980        0.6528             nan     0.0100   -0.0002
  1000        0.6503             nan     0.0100   -0.0001
  1020        0.6477             nan     0.0100   -0.0002
  1040        0.6453             nan     0.0100   -0.0001
  1060        0.6425             nan     0.0100   -0.0001
  1080        0.6407             nan     0.0100   -0.0001
  1100        0.6383             nan     0.0100   -0.0001

- Fold02.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2729             nan     0.1000    0.0272
     2        1.2284             nan     0.1000    0.0237
     3        1.1881             nan     0.1000    0.0188
     4        1.1604             nan     0.1000    0.0162
     5        1.1339             nan     0.1000    0.0124
     6        1.1084             nan     0.1000    0.0114
     7        1.0873             nan     0.1000    0.0091
     8        1.0697             nan     0.1000    0.0073
     9        1.0556             nan     0.1000    0.0067
    10        1.0428             nan     0.1000    0.0051
    20        0.9554             nan     0.1000    0.0008
    40        0.8805             nan     0.1000    0.0007
    60        0.8444             nan     0.1000    0.0002
    80        0.8215             nan     0.1000   -0.0015
   100        0.8042             nan     0.1000   -0.0011
   120        0.7932             nan     0.1000    0.0002
   140        0.7848             nan     0.1000   -0.0005
   160        0.7758             nan     0.1000   -0.0014
   180        0.7677             nan     0.1000   -0.0008
   200        0.7618             nan     0.1000   -0.0007
   220        0.7548             nan     0.1000   -0.0008
   240        0.7501             nan     0.1000   -0.0004
   260        0.7447             nan     0.1000   -0.0010
   280        0.7392             nan     0.1000   -0.0008
   300        0.7351             nan     0.1000   -0.0008
   320        0.7322             nan     0.1000   -0.0010
   340        0.7301             nan     0.1000   -0.0008
   360        0.7258             nan     0.1000   -0.0004
   380        0.7226             nan     0.1000   -0.0008
   400        0.7193             nan     0.1000   -0.0007
   420        0.7160             nan     0.1000   -0.0012
   440        0.7112             nan     0.1000   -0.0006
   460        0.7094             nan     0.1000   -0.0006
   480        0.7081             nan     0.1000   -0.0019
   500        0.7069             nan     0.1000   -0.0009
   520        0.7038             nan     0.1000   -0.0006
   540        0.7014             nan     0.1000   -0.0003
   560        0.7002             nan     0.1000   -0.0008
   580        0.6969             nan     0.1000   -0.0010
   600        0.6952             nan     0.1000   -0.0006
   620        0.6941             nan     0.1000   -0.0010
   640        0.6924             nan     0.1000   -0.0005
   660        0.6903             nan     0.1000   -0.0006
   680        0.6888             nan     0.1000   -0.0011
   700        0.6871             nan     0.1000   -0.0017
   720        0.6852             nan     0.1000   -0.0008
   740        0.6839             nan     0.1000   -0.0006
   760        0.6821             nan     0.1000   -0.0003
   780        0.6809             nan     0.1000   -0.0007
   800        0.6794             nan     0.1000   -0.0010
   820        0.6766             nan     0.1000   -0.0008
   840        0.6739             nan     0.1000   -0.0004
   860        0.6722             nan     0.1000   -0.0007
   880        0.6712             nan     0.1000   -0.0007
   900        0.6692             nan     0.1000   -0.0008
   920        0.6669             nan     0.1000   -0.0003
   940        0.6648             nan     0.1000   -0.0012
   960        0.6627             nan     0.1000   -0.0001
   980        0.6625             nan     0.1000   -0.0004
  1000        0.6605             nan     0.1000   -0.0007
  1020        0.6588             nan     0.1000   -0.0006
  1040        0.6575             nan     0.1000   -0.0003
  1060        0.6561             nan     0.1000   -0.0004
  1080        0.6544             nan     0.1000   -0.0007
  1100        0.6532             nan     0.1000   -0.0009

- Fold02.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2643             nan     0.1000    0.0350
     2        1.2107             nan     0.1000    0.0256
     3        1.1611             nan     0.1000    0.0255
     4        1.1210             nan     0.1000    0.0210
     5        1.0843             nan     0.1000    0.0174
     6        1.0591             nan     0.1000    0.0143
     7        1.0334             nan     0.1000    0.0135
     8        1.0079             nan     0.1000    0.0116
     9        0.9875             nan     0.1000    0.0082
    10        0.9683             nan     0.1000    0.0086
    20        0.8712             nan     0.1000    0.0012
    40        0.7998             nan     0.1000   -0.0003
    60        0.7641             nan     0.1000   -0.0006
    80        0.7342             nan     0.1000   -0.0005
   100        0.7149             nan     0.1000   -0.0010
   120        0.6972             nan     0.1000   -0.0014
   140        0.6811             nan     0.1000   -0.0016
   160        0.6612             nan     0.1000   -0.0006
   180        0.6499             nan     0.1000   -0.0014
   200        0.6347             nan     0.1000   -0.0009
   220        0.6233             nan     0.1000   -0.0014
   240        0.6136             nan     0.1000   -0.0009
   260        0.6009             nan     0.1000   -0.0007
   280        0.5912             nan     0.1000   -0.0011
   300        0.5773             nan     0.1000   -0.0004
   320        0.5713             nan     0.1000   -0.0007
   340        0.5634             nan     0.1000   -0.0008
   360        0.5558             nan     0.1000   -0.0003
   380        0.5489             nan     0.1000   -0.0007
   400        0.5442             nan     0.1000   -0.0010
   420        0.5380             nan     0.1000   -0.0010
   440        0.5319             nan     0.1000   -0.0007
   460        0.5265             nan     0.1000   -0.0012
   480        0.5213             nan     0.1000   -0.0008
   500        0.5143             nan     0.1000   -0.0006
   520        0.5093             nan     0.1000   -0.0011
   540        0.5044             nan     0.1000   -0.0003
   560        0.5004             nan     0.1000   -0.0008
   580        0.4932             nan     0.1000   -0.0014
   600        0.4873             nan     0.1000   -0.0006
   620        0.4812             nan     0.1000   -0.0006
   640        0.4782             nan     0.1000   -0.0012
   660        0.4732             nan     0.1000   -0.0016
   680        0.4697             nan     0.1000   -0.0009
   700        0.4630             nan     0.1000   -0.0009
   720        0.4584             nan     0.1000   -0.0011
   740        0.4546             nan     0.1000   -0.0008
   760        0.4514             nan     0.1000   -0.0005
   780        0.4472             nan     0.1000   -0.0012
   800        0.4440             nan     0.1000   -0.0008
   820        0.4405             nan     0.1000   -0.0012
   840        0.4370             nan     0.1000   -0.0011
   860        0.4313             nan     0.1000   -0.0009
   880        0.4277             nan     0.1000   -0.0005
   900        0.4242             nan     0.1000   -0.0007
   920        0.4208             nan     0.1000   -0.0005
   940        0.4177             nan     0.1000   -0.0006
   960        0.4154             nan     0.1000   -0.0009
   980        0.4124             nan     0.1000   -0.0007
  1000        0.4086             nan     0.1000   -0.0009
  1020        0.4035             nan     0.1000   -0.0010
  1040        0.4001             nan     0.1000   -0.0006
  1060        0.3968             nan     0.1000   -0.0013
  1080        0.3931             nan     0.1000   -0.0009
  1100        0.3915             nan     0.1000   -0.0006

- Fold02.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2578             nan     0.1000    0.0362
     2        1.1957             nan     0.1000    0.0305
     3        1.1439             nan     0.1000    0.0217
     4        1.1012             nan     0.1000    0.0212
     5        1.0573             nan     0.1000    0.0193
     6        1.0265             nan     0.1000    0.0161
     7        0.9970             nan     0.1000    0.0126
     8        0.9700             nan     0.1000    0.0104
     9        0.9524             nan     0.1000    0.0082
    10        0.9347             nan     0.1000    0.0074
    20        0.8324             nan     0.1000   -0.0008
    40        0.7543             nan     0.1000    0.0002
    60        0.7187             nan     0.1000   -0.0009
    80        0.6847             nan     0.1000   -0.0009
   100        0.6594             nan     0.1000   -0.0007
   120        0.6356             nan     0.1000   -0.0026
   140        0.6129             nan     0.1000   -0.0002
   160        0.5942             nan     0.1000   -0.0023
   180        0.5709             nan     0.1000   -0.0007
   200        0.5550             nan     0.1000   -0.0012
   220        0.5362             nan     0.1000   -0.0010
   240        0.5244             nan     0.1000   -0.0007
   260        0.5101             nan     0.1000   -0.0000
   280        0.5006             nan     0.1000   -0.0026
   300        0.4895             nan     0.1000   -0.0008
   320        0.4774             nan     0.1000   -0.0012
   340        0.4667             nan     0.1000   -0.0011
   360        0.4561             nan     0.1000   -0.0008
   380        0.4444             nan     0.1000   -0.0010
   400        0.4368             nan     0.1000   -0.0013
   420        0.4293             nan     0.1000   -0.0004
   440        0.4219             nan     0.1000   -0.0005
   460        0.4132             nan     0.1000   -0.0011
   480        0.4078             nan     0.1000   -0.0007
   500        0.3999             nan     0.1000   -0.0012
   520        0.3918             nan     0.1000   -0.0009
   540        0.3848             nan     0.1000   -0.0007
   560        0.3806             nan     0.1000   -0.0012
   580        0.3738             nan     0.1000   -0.0004
   600        0.3673             nan     0.1000   -0.0013
   620        0.3600             nan     0.1000   -0.0010
   640        0.3540             nan     0.1000   -0.0013
   660        0.3473             nan     0.1000   -0.0008
   680        0.3421             nan     0.1000   -0.0005
   700        0.3370             nan     0.1000   -0.0005
   720        0.3298             nan     0.1000   -0.0015
   740        0.3237             nan     0.1000   -0.0014
   760        0.3209             nan     0.1000   -0.0009
   780        0.3156             nan     0.1000   -0.0012
   800        0.3109             nan     0.1000   -0.0007
   820        0.3069             nan     0.1000   -0.0009
   840        0.3024             nan     0.1000   -0.0012
   860        0.2980             nan     0.1000   -0.0010
   880        0.2937             nan     0.1000   -0.0008
   900        0.2897             nan     0.1000   -0.0008
   920        0.2858             nan     0.1000   -0.0010
   940        0.2825             nan     0.1000   -0.0006
   960        0.2799             nan     0.1000   -0.0009
   980        0.2755             nan     0.1000   -0.0011
  1000        0.2707             nan     0.1000   -0.0006
  1020        0.2663             nan     0.1000   -0.0011
  1040        0.2636             nan     0.1000   -0.0008
  1060        0.2605             nan     0.1000   -0.0007
  1080        0.2573             nan     0.1000   -0.0009
  1100        0.2542             nan     0.1000   -0.0006

- Fold02.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3253             nan     0.0100    0.0031
     2        1.3194             nan     0.0100    0.0029
     3        1.3132             nan     0.0100    0.0030
     4        1.3074             nan     0.0100    0.0028
     5        1.3013             nan     0.0100    0.0029
     6        1.2958             nan     0.0100    0.0026
     7        1.2905             nan     0.0100    0.0028
     8        1.2852             nan     0.0100    0.0026
     9        1.2796             nan     0.0100    0.0027
    10        1.2745             nan     0.0100    0.0025
    20        1.2282             nan     0.0100    0.0022
    40        1.1568             nan     0.0100    0.0014
    60        1.1055             nan     0.0100    0.0011
    80        1.0700             nan     0.0100    0.0008
   100        1.0385             nan     0.0100    0.0006
   120        1.0131             nan     0.0100    0.0005
   140        0.9926             nan     0.0100    0.0003
   160        0.9746             nan     0.0100    0.0004
   180        0.9590             nan     0.0100    0.0002
   200        0.9448             nan     0.0100    0.0003
   220        0.9319             nan     0.0100    0.0001
   240        0.9205             nan     0.0100    0.0002
   260        0.9110             nan     0.0100    0.0001
   280        0.9021             nan     0.0100    0.0001
   300        0.8933             nan     0.0100    0.0001
   320        0.8858             nan     0.0100   -0.0000
   340        0.8789             nan     0.0100    0.0001
   360        0.8732             nan     0.0100    0.0001
   380        0.8670             nan     0.0100    0.0001
   400        0.8617             nan     0.0100    0.0000
   420        0.8569             nan     0.0100    0.0000
   440        0.8523             nan     0.0100    0.0001
   460        0.8478             nan     0.0100   -0.0000
   480        0.8435             nan     0.0100   -0.0000
   500        0.8395             nan     0.0100    0.0001
   520        0.8355             nan     0.0100    0.0000
   540        0.8317             nan     0.0100    0.0000
   560        0.8284             nan     0.0100   -0.0000
   580        0.8252             nan     0.0100    0.0000
   600        0.8219             nan     0.0100    0.0000
   620        0.8188             nan     0.0100    0.0000
   640        0.8162             nan     0.0100   -0.0000
   660        0.8134             nan     0.0100    0.0000
   680        0.8109             nan     0.0100   -0.0000
   700        0.8083             nan     0.0100    0.0000
   720        0.8057             nan     0.0100    0.0000
   740        0.8034             nan     0.0100    0.0000
   760        0.8013             nan     0.0100    0.0000
   780        0.7991             nan     0.0100   -0.0000
   800        0.7972             nan     0.0100   -0.0000
   820        0.7953             nan     0.0100   -0.0000
   840        0.7933             nan     0.0100   -0.0000
   860        0.7918             nan     0.0100   -0.0001
   880        0.7900             nan     0.0100   -0.0000
   900        0.7883             nan     0.0100   -0.0002
   920        0.7865             nan     0.0100   -0.0000
   940        0.7850             nan     0.0100   -0.0000
   960        0.7832             nan     0.0100    0.0000
   980        0.7816             nan     0.0100    0.0000
  1000        0.7800             nan     0.0100   -0.0001
  1020        0.7785             nan     0.0100   -0.0000
  1040        0.7771             nan     0.0100   -0.0001
  1060        0.7758             nan     0.0100   -0.0001
  1080        0.7744             nan     0.0100   -0.0000
  1100        0.7730             nan     0.0100   -0.0001

- Fold03.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3240             nan     0.0100    0.0037
     2        1.3164             nan     0.0100    0.0035
     3        1.3085             nan     0.0100    0.0036
     4        1.3011             nan     0.0100    0.0037
     5        1.2935             nan     0.0100    0.0037
     6        1.2864             nan     0.0100    0.0034
     7        1.2791             nan     0.0100    0.0034
     8        1.2717             nan     0.0100    0.0033
     9        1.2651             nan     0.0100    0.0032
    10        1.2584             nan     0.0100    0.0030
    20        1.2005             nan     0.0100    0.0027
    40        1.1104             nan     0.0100    0.0019
    60        1.0445             nan     0.0100    0.0013
    80        0.9942             nan     0.0100    0.0010
   100        0.9572             nan     0.0100    0.0006
   120        0.9270             nan     0.0100    0.0006
   140        0.9035             nan     0.0100    0.0004
   160        0.8843             nan     0.0100    0.0004
   180        0.8687             nan     0.0100    0.0002
   200        0.8551             nan     0.0100    0.0002
   220        0.8430             nan     0.0100    0.0002
   240        0.8326             nan     0.0100    0.0000
   260        0.8232             nan     0.0100    0.0000
   280        0.8147             nan     0.0100    0.0001
   300        0.8077             nan     0.0100    0.0000
   320        0.8005             nan     0.0100   -0.0000
   340        0.7939             nan     0.0100    0.0000
   360        0.7886             nan     0.0100   -0.0000
   380        0.7831             nan     0.0100    0.0000
   400        0.7776             nan     0.0100    0.0000
   420        0.7731             nan     0.0100    0.0000
   440        0.7689             nan     0.0100   -0.0000
   460        0.7642             nan     0.0100   -0.0001
   480        0.7598             nan     0.0100   -0.0000
   500        0.7563             nan     0.0100   -0.0001
   520        0.7531             nan     0.0100   -0.0000
   540        0.7497             nan     0.0100   -0.0000
   560        0.7464             nan     0.0100   -0.0001
   580        0.7434             nan     0.0100   -0.0001
   600        0.7402             nan     0.0100   -0.0001
   620        0.7374             nan     0.0100    0.0001
   640        0.7344             nan     0.0100   -0.0000
   660        0.7317             nan     0.0100   -0.0001
   680        0.7285             nan     0.0100   -0.0003
   700        0.7257             nan     0.0100   -0.0001
   720        0.7232             nan     0.0100   -0.0001
   740        0.7208             nan     0.0100   -0.0001
   760        0.7183             nan     0.0100   -0.0000
   780        0.7162             nan     0.0100   -0.0000
   800        0.7140             nan     0.0100   -0.0000
   820        0.7119             nan     0.0100   -0.0001
   840        0.7099             nan     0.0100   -0.0001
   860        0.7074             nan     0.0100   -0.0001
   880        0.7054             nan     0.0100   -0.0001
   900        0.7035             nan     0.0100   -0.0001
   920        0.7011             nan     0.0100   -0.0000
   940        0.6992             nan     0.0100   -0.0001
   960        0.6973             nan     0.0100   -0.0000
   980        0.6956             nan     0.0100   -0.0002
  1000        0.6938             nan     0.0100   -0.0001
  1020        0.6919             nan     0.0100   -0.0001
  1040        0.6899             nan     0.0100   -0.0001
  1060        0.6882             nan     0.0100   -0.0001
  1080        0.6862             nan     0.0100   -0.0001
  1100        0.6841             nan     0.0100   -0.0001

- Fold03.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0042
     2        1.3142             nan     0.0100    0.0039
     3        1.3054             nan     0.0100    0.0039
     4        1.2976             nan     0.0100    0.0037
     5        1.2899             nan     0.0100    0.0038
     6        1.2820             nan     0.0100    0.0039
     7        1.2738             nan     0.0100    0.0039
     8        1.2667             nan     0.0100    0.0039
     9        1.2587             nan     0.0100    0.0039
    10        1.2514             nan     0.0100    0.0036
    20        1.1884             nan     0.0100    0.0026
    40        1.0885             nan     0.0100    0.0017
    60        1.0170             nan     0.0100    0.0014
    80        0.9632             nan     0.0100    0.0011
   100        0.9211             nan     0.0100    0.0008
   120        0.8899             nan     0.0100    0.0005
   140        0.8665             nan     0.0100    0.0002
   160        0.8465             nan     0.0100    0.0004
   180        0.8302             nan     0.0100    0.0003
   200        0.8159             nan     0.0100    0.0002
   220        0.8033             nan     0.0100    0.0001
   240        0.7923             nan     0.0100    0.0001
   260        0.7829             nan     0.0100    0.0000
   280        0.7747             nan     0.0100   -0.0000
   300        0.7671             nan     0.0100   -0.0001
   320        0.7611             nan     0.0100   -0.0000
   340        0.7536             nan     0.0100   -0.0000
   360        0.7469             nan     0.0100   -0.0000
   380        0.7412             nan     0.0100   -0.0000
   400        0.7357             nan     0.0100   -0.0002
   420        0.7301             nan     0.0100    0.0000
   440        0.7256             nan     0.0100   -0.0000
   460        0.7207             nan     0.0100   -0.0001
   480        0.7163             nan     0.0100    0.0001
   500        0.7118             nan     0.0100   -0.0001
   520        0.7080             nan     0.0100   -0.0001
   540        0.7043             nan     0.0100   -0.0000
   560        0.7002             nan     0.0100   -0.0001
   580        0.6961             nan     0.0100   -0.0001
   600        0.6924             nan     0.0100   -0.0001
   620        0.6883             nan     0.0100   -0.0000
   640        0.6851             nan     0.0100   -0.0001
   660        0.6817             nan     0.0100   -0.0002
   680        0.6786             nan     0.0100   -0.0001
   700        0.6752             nan     0.0100   -0.0001
   720        0.6720             nan     0.0100   -0.0002
   740        0.6688             nan     0.0100   -0.0001
   760        0.6659             nan     0.0100   -0.0001
   780        0.6631             nan     0.0100   -0.0001
   800        0.6603             nan     0.0100   -0.0001
   820        0.6576             nan     0.0100   -0.0001
   840        0.6545             nan     0.0100   -0.0001
   860        0.6517             nan     0.0100    0.0000
   880        0.6489             nan     0.0100   -0.0001
   900        0.6463             nan     0.0100   -0.0001
   920        0.6437             nan     0.0100   -0.0001
   940        0.6412             nan     0.0100   -0.0000
   960        0.6387             nan     0.0100   -0.0001
   980        0.6365             nan     0.0100   -0.0002
  1000        0.6340             nan     0.0100   -0.0001
  1020        0.6315             nan     0.0100   -0.0000
  1040        0.6288             nan     0.0100   -0.0001
  1060        0.6265             nan     0.0100   -0.0002
  1080        0.6242             nan     0.0100   -0.0001
  1100        0.6218             nan     0.0100   -0.0001

- Fold03.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2717             nan     0.1000    0.0297
     2        1.2242             nan     0.1000    0.0244
     3        1.1826             nan     0.1000    0.0196
     4        1.1508             nan     0.1000    0.0156
     5        1.1276             nan     0.1000    0.0129
     6        1.1030             nan     0.1000    0.0115
     7        1.0881             nan     0.1000    0.0065
     8        1.0667             nan     0.1000    0.0083
     9        1.0509             nan     0.1000    0.0068
    10        1.0337             nan     0.1000    0.0069
    20        0.9410             nan     0.1000    0.0021
    40        0.8618             nan     0.1000    0.0006
    60        0.8202             nan     0.1000   -0.0002
    80        0.7962             nan     0.1000   -0.0002
   100        0.7810             nan     0.1000    0.0002
   120        0.7689             nan     0.1000   -0.0005
   140        0.7611             nan     0.1000   -0.0015
   160        0.7502             nan     0.1000    0.0000
   180        0.7426             nan     0.1000   -0.0004
   200        0.7356             nan     0.1000   -0.0007
   220        0.7287             nan     0.1000   -0.0005
   240        0.7222             nan     0.1000   -0.0004
   260        0.7184             nan     0.1000   -0.0004
   280        0.7149             nan     0.1000   -0.0007
   300        0.7108             nan     0.1000   -0.0006
   320        0.7079             nan     0.1000   -0.0008
   340        0.7041             nan     0.1000   -0.0011
   360        0.7009             nan     0.1000   -0.0012
   380        0.6958             nan     0.1000   -0.0002
   400        0.6930             nan     0.1000   -0.0005
   420        0.6913             nan     0.1000   -0.0010
   440        0.6880             nan     0.1000   -0.0006
   460        0.6851             nan     0.1000   -0.0003
   480        0.6824             nan     0.1000   -0.0007
   500        0.6804             nan     0.1000   -0.0007
   520        0.6782             nan     0.1000   -0.0016
   540        0.6765             nan     0.1000   -0.0005
   560        0.6739             nan     0.1000   -0.0006
   580        0.6725             nan     0.1000   -0.0008
   600        0.6700             nan     0.1000   -0.0013
   620        0.6679             nan     0.1000   -0.0005
   640        0.6670             nan     0.1000   -0.0008
   660        0.6650             nan     0.1000   -0.0008
   680        0.6628             nan     0.1000   -0.0006
   700        0.6603             nan     0.1000   -0.0003
   720        0.6599             nan     0.1000   -0.0013
   740        0.6579             nan     0.1000   -0.0012
   760        0.6556             nan     0.1000   -0.0007
   780        0.6534             nan     0.1000   -0.0006
   800        0.6527             nan     0.1000   -0.0015
   820        0.6515             nan     0.1000   -0.0014
   840        0.6499             nan     0.1000   -0.0008
   860        0.6475             nan     0.1000   -0.0006
   880        0.6450             nan     0.1000   -0.0008
   900        0.6443             nan     0.1000   -0.0005
   920        0.6431             nan     0.1000   -0.0006
   940        0.6420             nan     0.1000   -0.0004
   960        0.6407             nan     0.1000   -0.0008
   980        0.6388             nan     0.1000   -0.0009
  1000        0.6378             nan     0.1000   -0.0006
  1020        0.6359             nan     0.1000   -0.0007
  1040        0.6349             nan     0.1000   -0.0007
  1060        0.6345             nan     0.1000   -0.0013
  1080        0.6329             nan     0.1000   -0.0003
  1100        0.6316             nan     0.1000   -0.0009

- Fold03.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2574             nan     0.1000    0.0355
     2        1.1965             nan     0.1000    0.0282
     3        1.1465             nan     0.1000    0.0263
     4        1.1040             nan     0.1000    0.0204
     5        1.0692             nan     0.1000    0.0179
     6        1.0368             nan     0.1000    0.0154
     7        1.0090             nan     0.1000    0.0124
     8        0.9849             nan     0.1000    0.0102
     9        0.9628             nan     0.1000    0.0092
    10        0.9447             nan     0.1000    0.0073
    20        0.8535             nan     0.1000    0.0021
    40        0.7753             nan     0.1000    0.0005
    60        0.7368             nan     0.1000   -0.0010
    80        0.7155             nan     0.1000   -0.0008
   100        0.6990             nan     0.1000   -0.0007
   120        0.6818             nan     0.1000   -0.0012
   140        0.6610             nan     0.1000    0.0001
   160        0.6468             nan     0.1000   -0.0019
   180        0.6346             nan     0.1000   -0.0013
   200        0.6205             nan     0.1000   -0.0006
   220        0.6092             nan     0.1000   -0.0009
   240        0.6024             nan     0.1000   -0.0012
   260        0.5926             nan     0.1000   -0.0007
   280        0.5842             nan     0.1000   -0.0011
   300        0.5752             nan     0.1000   -0.0011
   320        0.5678             nan     0.1000   -0.0013
   340        0.5588             nan     0.1000   -0.0009
   360        0.5522             nan     0.1000   -0.0003
   380        0.5462             nan     0.1000   -0.0010
   400        0.5395             nan     0.1000   -0.0013
   420        0.5318             nan     0.1000   -0.0006
   440        0.5264             nan     0.1000   -0.0011
   460        0.5191             nan     0.1000   -0.0008
   480        0.5111             nan     0.1000   -0.0007
   500        0.5053             nan     0.1000   -0.0014
   520        0.5005             nan     0.1000   -0.0007
   540        0.4947             nan     0.1000   -0.0005
   560        0.4905             nan     0.1000   -0.0013
   580        0.4841             nan     0.1000   -0.0009
   600        0.4792             nan     0.1000   -0.0011
   620        0.4739             nan     0.1000   -0.0005
   640        0.4682             nan     0.1000   -0.0014
   660        0.4647             nan     0.1000   -0.0008
   680        0.4601             nan     0.1000   -0.0013
   700        0.4558             nan     0.1000   -0.0014
   720        0.4516             nan     0.1000   -0.0013
   740        0.4466             nan     0.1000   -0.0007
   760        0.4425             nan     0.1000   -0.0014
   780        0.4398             nan     0.1000   -0.0009
   800        0.4357             nan     0.1000   -0.0009
   820        0.4308             nan     0.1000   -0.0017
   840        0.4261             nan     0.1000   -0.0010
   860        0.4240             nan     0.1000   -0.0007
   880        0.4202             nan     0.1000   -0.0012
   900        0.4180             nan     0.1000   -0.0006
   920        0.4136             nan     0.1000   -0.0006
   940        0.4121             nan     0.1000   -0.0006
   960        0.4086             nan     0.1000   -0.0014
   980        0.4053             nan     0.1000   -0.0009
  1000        0.4029             nan     0.1000   -0.0010
  1020        0.3999             nan     0.1000   -0.0018
  1040        0.3973             nan     0.1000   -0.0005
  1060        0.3922             nan     0.1000   -0.0005
  1080        0.3895             nan     0.1000   -0.0014
  1100        0.3872             nan     0.1000   -0.0008

- Fold03.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2546             nan     0.1000    0.0402
     2        1.1934             nan     0.1000    0.0284
     3        1.1365             nan     0.1000    0.0263
     4        1.0862             nan     0.1000    0.0226
     5        1.0437             nan     0.1000    0.0193
     6        1.0092             nan     0.1000    0.0154
     7        0.9816             nan     0.1000    0.0138
     8        0.9585             nan     0.1000    0.0097
     9        0.9351             nan     0.1000    0.0091
    10        0.9175             nan     0.1000    0.0075
    20        0.8178             nan     0.1000    0.0027
    40        0.7390             nan     0.1000   -0.0011
    60        0.6981             nan     0.1000   -0.0005
    80        0.6666             nan     0.1000   -0.0008
   100        0.6413             nan     0.1000   -0.0025
   120        0.6158             nan     0.1000   -0.0009
   140        0.5959             nan     0.1000   -0.0010
   160        0.5782             nan     0.1000   -0.0010
   180        0.5616             nan     0.1000   -0.0013
   200        0.5458             nan     0.1000   -0.0008
   220        0.5324             nan     0.1000   -0.0024
   240        0.5156             nan     0.1000   -0.0008
   260        0.5054             nan     0.1000   -0.0014
   280        0.4933             nan     0.1000   -0.0018
   300        0.4804             nan     0.1000   -0.0008
   320        0.4692             nan     0.1000   -0.0012
   340        0.4570             nan     0.1000   -0.0009
   360        0.4494             nan     0.1000   -0.0011
   380        0.4427             nan     0.1000   -0.0014
   400        0.4324             nan     0.1000   -0.0001
   420        0.4245             nan     0.1000   -0.0010
   440        0.4152             nan     0.1000   -0.0012
   460        0.4079             nan     0.1000   -0.0011
   480        0.3999             nan     0.1000   -0.0006
   500        0.3919             nan     0.1000   -0.0013
   520        0.3869             nan     0.1000   -0.0008
   540        0.3801             nan     0.1000   -0.0008
   560        0.3746             nan     0.1000   -0.0008
   580        0.3677             nan     0.1000   -0.0007
   600        0.3618             nan     0.1000   -0.0017
   620        0.3553             nan     0.1000   -0.0005
   640        0.3497             nan     0.1000   -0.0014
   660        0.3435             nan     0.1000   -0.0007
   680        0.3395             nan     0.1000   -0.0012
   700        0.3350             nan     0.1000   -0.0006
   720        0.3307             nan     0.1000   -0.0011
   740        0.3263             nan     0.1000   -0.0007
   760        0.3234             nan     0.1000   -0.0014
   780        0.3191             nan     0.1000   -0.0011
   800        0.3143             nan     0.1000   -0.0009
   820        0.3119             nan     0.1000   -0.0008
   840        0.3061             nan     0.1000   -0.0005
   860        0.3020             nan     0.1000   -0.0010
   880        0.2972             nan     0.1000   -0.0007
   900        0.2921             nan     0.1000   -0.0007
   920        0.2894             nan     0.1000   -0.0011
   940        0.2858             nan     0.1000   -0.0005
   960        0.2823             nan     0.1000   -0.0005
   980        0.2787             nan     0.1000   -0.0007
  1000        0.2749             nan     0.1000   -0.0005
  1020        0.2710             nan     0.1000   -0.0005
  1040        0.2681             nan     0.1000   -0.0010
  1060        0.2644             nan     0.1000   -0.0006
  1080        0.2624             nan     0.1000   -0.0010
  1100        0.2594             nan     0.1000   -0.0008

- Fold03.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3252             nan     0.0100    0.0030
     2        1.3191             nan     0.0100    0.0029
     3        1.3135             nan     0.0100    0.0027
     4        1.3081             nan     0.0100    0.0027
     5        1.3026             nan     0.0100    0.0027
     6        1.2975             nan     0.0100    0.0027
     7        1.2926             nan     0.0100    0.0025
     8        1.2872             nan     0.0100    0.0026
     9        1.2819             nan     0.0100    0.0025
    10        1.2772             nan     0.0100    0.0025
    20        1.2336             nan     0.0100    0.0020
    40        1.1656             nan     0.0100    0.0014
    60        1.1184             nan     0.0100    0.0010
    80        1.0791             nan     0.0100    0.0008
   100        1.0480             nan     0.0100    0.0006
   120        1.0222             nan     0.0100    0.0005
   140        1.0010             nan     0.0100    0.0005
   160        0.9816             nan     0.0100    0.0004
   180        0.9657             nan     0.0100    0.0003
   200        0.9521             nan     0.0100    0.0003
   220        0.9391             nan     0.0100    0.0002
   240        0.9283             nan     0.0100    0.0002
   260        0.9191             nan     0.0100    0.0001
   280        0.9100             nan     0.0100    0.0002
   300        0.9020             nan     0.0100    0.0001
   320        0.8947             nan     0.0100    0.0001
   340        0.8877             nan     0.0100    0.0001
   360        0.8811             nan     0.0100    0.0001
   380        0.8755             nan     0.0100    0.0000
   400        0.8701             nan     0.0100    0.0001
   420        0.8652             nan     0.0100    0.0001
   440        0.8603             nan     0.0100    0.0001
   460        0.8558             nan     0.0100    0.0000
   480        0.8511             nan     0.0100    0.0000
   500        0.8471             nan     0.0100    0.0001
   520        0.8432             nan     0.0100    0.0000
   540        0.8393             nan     0.0100   -0.0000
   560        0.8358             nan     0.0100    0.0000
   580        0.8321             nan     0.0100    0.0000
   600        0.8289             nan     0.0100    0.0000
   620        0.8258             nan     0.0100   -0.0000
   640        0.8231             nan     0.0100   -0.0000
   660        0.8202             nan     0.0100   -0.0000
   680        0.8178             nan     0.0100    0.0001
   700        0.8148             nan     0.0100    0.0000
   720        0.8123             nan     0.0100    0.0000
   740        0.8098             nan     0.0100   -0.0000
   760        0.8073             nan     0.0100    0.0000
   780        0.8049             nan     0.0100    0.0000
   800        0.8024             nan     0.0100   -0.0000
   820        0.8002             nan     0.0100   -0.0000
   840        0.7983             nan     0.0100   -0.0000
   860        0.7961             nan     0.0100   -0.0000
   880        0.7939             nan     0.0100   -0.0000
   900        0.7923             nan     0.0100   -0.0000
   920        0.7907             nan     0.0100   -0.0001
   940        0.7890             nan     0.0100   -0.0001
   960        0.7871             nan     0.0100   -0.0000
   980        0.7854             nan     0.0100   -0.0000
  1000        0.7836             nan     0.0100    0.0000
  1020        0.7821             nan     0.0100    0.0000
  1040        0.7806             nan     0.0100   -0.0000
  1060        0.7789             nan     0.0100   -0.0000
  1080        0.7776             nan     0.0100   -0.0001
  1100        0.7763             nan     0.0100   -0.0000

- Fold04.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3240             nan     0.0100    0.0036
     2        1.3165             nan     0.0100    0.0036
     3        1.3091             nan     0.0100    0.0035
     4        1.3022             nan     0.0100    0.0033
     5        1.2954             nan     0.0100    0.0029
     6        1.2891             nan     0.0100    0.0033
     7        1.2824             nan     0.0100    0.0032
     8        1.2761             nan     0.0100    0.0032
     9        1.2698             nan     0.0100    0.0033
    10        1.2635             nan     0.0100    0.0030
    20        1.2070             nan     0.0100    0.0025
    40        1.1186             nan     0.0100    0.0018
    60        1.0550             nan     0.0100    0.0013
    80        1.0073             nan     0.0100    0.0006
   100        0.9693             nan     0.0100    0.0008
   120        0.9409             nan     0.0100    0.0007
   140        0.9165             nan     0.0100    0.0005
   160        0.8982             nan     0.0100    0.0003
   180        0.8824             nan     0.0100    0.0004
   200        0.8691             nan     0.0100    0.0002
   220        0.8557             nan     0.0100    0.0002
   240        0.8446             nan     0.0100    0.0002
   260        0.8349             nan     0.0100    0.0001
   280        0.8258             nan     0.0100    0.0002
   300        0.8178             nan     0.0100   -0.0000
   320        0.8100             nan     0.0100    0.0001
   340        0.8037             nan     0.0100    0.0000
   360        0.7971             nan     0.0100    0.0000
   380        0.7913             nan     0.0100    0.0000
   400        0.7859             nan     0.0100   -0.0001
   420        0.7806             nan     0.0100    0.0000
   440        0.7759             nan     0.0100    0.0000
   460        0.7711             nan     0.0100   -0.0000
   480        0.7672             nan     0.0100   -0.0001
   500        0.7633             nan     0.0100    0.0000
   520        0.7596             nan     0.0100   -0.0001
   540        0.7558             nan     0.0100   -0.0001
   560        0.7517             nan     0.0100   -0.0000
   580        0.7487             nan     0.0100   -0.0001
   600        0.7458             nan     0.0100    0.0000
   620        0.7427             nan     0.0100    0.0000
   640        0.7396             nan     0.0100   -0.0001
   660        0.7369             nan     0.0100   -0.0000
   680        0.7346             nan     0.0100   -0.0001
   700        0.7316             nan     0.0100   -0.0000
   720        0.7288             nan     0.0100   -0.0001
   740        0.7263             nan     0.0100   -0.0000
   760        0.7236             nan     0.0100   -0.0000
   780        0.7209             nan     0.0100   -0.0001
   800        0.7185             nan     0.0100   -0.0001
   820        0.7158             nan     0.0100   -0.0001
   840        0.7133             nan     0.0100   -0.0001
   860        0.7115             nan     0.0100   -0.0001
   880        0.7094             nan     0.0100   -0.0001
   900        0.7071             nan     0.0100   -0.0002
   920        0.7050             nan     0.0100   -0.0000
   940        0.7028             nan     0.0100   -0.0001
   960        0.7007             nan     0.0100   -0.0000
   980        0.6988             nan     0.0100   -0.0001
  1000        0.6967             nan     0.0100   -0.0001
  1020        0.6950             nan     0.0100   -0.0001
  1040        0.6933             nan     0.0100   -0.0001
  1060        0.6915             nan     0.0100   -0.0000
  1080        0.6895             nan     0.0100   -0.0001
  1100        0.6877             nan     0.0100   -0.0001

- Fold04.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0040
     2        1.3152             nan     0.0100    0.0040
     3        1.3068             nan     0.0100    0.0040
     4        1.2990             nan     0.0100    0.0037
     5        1.2916             nan     0.0100    0.0037
     6        1.2835             nan     0.0100    0.0035
     7        1.2755             nan     0.0100    0.0036
     8        1.2686             nan     0.0100    0.0036
     9        1.2611             nan     0.0100    0.0033
    10        1.2539             nan     0.0100    0.0032
    20        1.1919             nan     0.0100    0.0027
    40        1.0963             nan     0.0100    0.0014
    60        1.0270             nan     0.0100    0.0012
    80        0.9752             nan     0.0100    0.0008
   100        0.9341             nan     0.0100    0.0008
   120        0.9022             nan     0.0100    0.0006
   140        0.8774             nan     0.0100    0.0004
   160        0.8575             nan     0.0100    0.0003
   180        0.8411             nan     0.0100    0.0001
   200        0.8263             nan     0.0100    0.0002
   220        0.8138             nan     0.0100   -0.0000
   240        0.8029             nan     0.0100    0.0000
   260        0.7938             nan     0.0100   -0.0001
   280        0.7853             nan     0.0100   -0.0000
   300        0.7772             nan     0.0100    0.0000
   320        0.7696             nan     0.0100   -0.0001
   340        0.7624             nan     0.0100    0.0001
   360        0.7557             nan     0.0100   -0.0000
   380        0.7496             nan     0.0100   -0.0001
   400        0.7438             nan     0.0100   -0.0001
   420        0.7383             nan     0.0100   -0.0002
   440        0.7325             nan     0.0100   -0.0001
   460        0.7275             nan     0.0100   -0.0000
   480        0.7229             nan     0.0100   -0.0001
   500        0.7185             nan     0.0100   -0.0001
   520        0.7140             nan     0.0100   -0.0000
   540        0.7094             nan     0.0100    0.0000
   560        0.7051             nan     0.0100    0.0000
   580        0.7014             nan     0.0100   -0.0002
   600        0.6978             nan     0.0100   -0.0001
   620        0.6933             nan     0.0100   -0.0002
   640        0.6897             nan     0.0100   -0.0001
   660        0.6858             nan     0.0100    0.0000
   680        0.6823             nan     0.0100   -0.0002
   700        0.6785             nan     0.0100    0.0000
   720        0.6749             nan     0.0100   -0.0000
   740        0.6715             nan     0.0100   -0.0002
   760        0.6686             nan     0.0100   -0.0001
   780        0.6659             nan     0.0100   -0.0000
   800        0.6634             nan     0.0100   -0.0002
   820        0.6605             nan     0.0100    0.0000
   840        0.6577             nan     0.0100   -0.0002
   860        0.6546             nan     0.0100   -0.0000
   880        0.6519             nan     0.0100   -0.0002
   900        0.6492             nan     0.0100   -0.0001
   920        0.6462             nan     0.0100   -0.0001
   940        0.6435             nan     0.0100   -0.0001
   960        0.6412             nan     0.0100   -0.0001
   980        0.6384             nan     0.0100   -0.0001
  1000        0.6360             nan     0.0100   -0.0002
  1020        0.6337             nan     0.0100   -0.0001
  1040        0.6312             nan     0.0100   -0.0001
  1060        0.6289             nan     0.0100   -0.0001
  1080        0.6265             nan     0.0100   -0.0000
  1100        0.6243             nan     0.0100   -0.0001

- Fold04.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2702             nan     0.1000    0.0267
     2        1.2260             nan     0.1000    0.0229
     3        1.1902             nan     0.1000    0.0173
     4        1.1593             nan     0.1000    0.0157
     5        1.1323             nan     0.1000    0.0131
     6        1.1120             nan     0.1000    0.0085
     7        1.0902             nan     0.1000    0.0102
     8        1.0721             nan     0.1000    0.0084
     9        1.0566             nan     0.1000    0.0073
    10        1.0412             nan     0.1000    0.0071
    20        0.9502             nan     0.1000    0.0020
    40        0.8674             nan     0.1000    0.0008
    60        0.8309             nan     0.1000   -0.0002
    80        0.8039             nan     0.1000   -0.0003
   100        0.7842             nan     0.1000   -0.0003
   120        0.7679             nan     0.1000   -0.0007
   140        0.7584             nan     0.1000   -0.0008
   160        0.7488             nan     0.1000   -0.0003
   180        0.7414             nan     0.1000   -0.0006
   200        0.7343             nan     0.1000   -0.0004
   220        0.7279             nan     0.1000   -0.0001
   240        0.7215             nan     0.1000   -0.0009
   260        0.7172             nan     0.1000   -0.0003
   280        0.7117             nan     0.1000   -0.0005
   300        0.7083             nan     0.1000   -0.0006
   320        0.7030             nan     0.1000   -0.0012
   340        0.6988             nan     0.1000   -0.0005
   360        0.6952             nan     0.1000   -0.0005
   380        0.6927             nan     0.1000   -0.0010
   400        0.6893             nan     0.1000   -0.0008
   420        0.6865             nan     0.1000   -0.0007
   440        0.6830             nan     0.1000   -0.0013
   460        0.6810             nan     0.1000   -0.0009
   480        0.6782             nan     0.1000   -0.0013
   500        0.6765             nan     0.1000   -0.0006
   520        0.6742             nan     0.1000   -0.0007
   540        0.6728             nan     0.1000   -0.0005
   560        0.6708             nan     0.1000   -0.0012
   580        0.6688             nan     0.1000   -0.0012
   600        0.6672             nan     0.1000   -0.0009
   620        0.6655             nan     0.1000   -0.0005
   640        0.6640             nan     0.1000   -0.0006
   660        0.6618             nan     0.1000   -0.0006
   680        0.6604             nan     0.1000   -0.0006
   700        0.6582             nan     0.1000   -0.0006
   720        0.6564             nan     0.1000   -0.0005
   740        0.6544             nan     0.1000   -0.0013
   760        0.6527             nan     0.1000   -0.0009
   780        0.6509             nan     0.1000   -0.0005
   800        0.6497             nan     0.1000   -0.0012
   820        0.6483             nan     0.1000   -0.0007
   840        0.6462             nan     0.1000   -0.0002
   860        0.6451             nan     0.1000   -0.0005
   880        0.6439             nan     0.1000   -0.0010
   900        0.6420             nan     0.1000   -0.0012
   920        0.6415             nan     0.1000   -0.0008
   940        0.6399             nan     0.1000   -0.0007
   960        0.6381             nan     0.1000   -0.0014
   980        0.6357             nan     0.1000   -0.0007
  1000        0.6337             nan     0.1000   -0.0003
  1020        0.6334             nan     0.1000   -0.0007
  1040        0.6323             nan     0.1000   -0.0010
  1060        0.6302             nan     0.1000   -0.0011
  1080        0.6297             nan     0.1000   -0.0011
  1100        0.6281             nan     0.1000   -0.0003

- Fold04.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2567             nan     0.1000    0.0319
     2        1.1937             nan     0.1000    0.0294
     3        1.1440             nan     0.1000    0.0238
     4        1.1071             nan     0.1000    0.0200
     5        1.0731             nan     0.1000    0.0162
     6        1.0473             nan     0.1000    0.0128
     7        1.0230             nan     0.1000    0.0125
     8        0.9993             nan     0.1000    0.0102
     9        0.9808             nan     0.1000    0.0088
    10        0.9626             nan     0.1000    0.0080
    20        0.8629             nan     0.1000    0.0014
    40        0.7837             nan     0.1000    0.0001
    60        0.7442             nan     0.1000   -0.0008
    80        0.7149             nan     0.1000   -0.0004
   100        0.6976             nan     0.1000   -0.0011
   120        0.6790             nan     0.1000   -0.0010
   140        0.6654             nan     0.1000   -0.0012
   160        0.6515             nan     0.1000   -0.0014
   180        0.6382             nan     0.1000   -0.0012
   200        0.6252             nan     0.1000   -0.0010
   220        0.6162             nan     0.1000   -0.0005
   240        0.6055             nan     0.1000   -0.0007
   260        0.5965             nan     0.1000   -0.0027
   280        0.5872             nan     0.1000   -0.0010
   300        0.5770             nan     0.1000   -0.0007
   320        0.5657             nan     0.1000   -0.0011
   340        0.5593             nan     0.1000   -0.0009
   360        0.5520             nan     0.1000   -0.0011
   380        0.5454             nan     0.1000   -0.0008
   400        0.5408             nan     0.1000   -0.0022
   420        0.5345             nan     0.1000   -0.0004
   440        0.5274             nan     0.1000   -0.0012
   460        0.5193             nan     0.1000   -0.0005
   480        0.5128             nan     0.1000   -0.0002
   500        0.5079             nan     0.1000   -0.0006
   520        0.5036             nan     0.1000   -0.0011
   540        0.4961             nan     0.1000   -0.0015
   560        0.4900             nan     0.1000   -0.0009
   580        0.4846             nan     0.1000   -0.0014
   600        0.4786             nan     0.1000   -0.0010
   620        0.4721             nan     0.1000   -0.0010
   640        0.4686             nan     0.1000   -0.0012
   660        0.4638             nan     0.1000   -0.0009
   680        0.4591             nan     0.1000   -0.0008
   700        0.4535             nan     0.1000   -0.0001
   720        0.4509             nan     0.1000   -0.0014
   740        0.4442             nan     0.1000   -0.0006
   760        0.4390             nan     0.1000   -0.0006
   780        0.4336             nan     0.1000   -0.0012
   800        0.4312             nan     0.1000   -0.0006
   820        0.4281             nan     0.1000   -0.0005
   840        0.4239             nan     0.1000   -0.0009
   860        0.4176             nan     0.1000   -0.0005
   880        0.4150             nan     0.1000   -0.0011
   900        0.4114             nan     0.1000   -0.0007
   920        0.4084             nan     0.1000   -0.0010
   940        0.4046             nan     0.1000   -0.0008
   960        0.4013             nan     0.1000   -0.0014
   980        0.3972             nan     0.1000   -0.0008
  1000        0.3950             nan     0.1000   -0.0010
  1020        0.3917             nan     0.1000   -0.0005
  1040        0.3882             nan     0.1000   -0.0005
  1060        0.3844             nan     0.1000   -0.0008
  1080        0.3820             nan     0.1000   -0.0006
  1100        0.3792             nan     0.1000   -0.0009

- Fold04.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2540             nan     0.1000    0.0406
     2        1.1888             nan     0.1000    0.0309
     3        1.1378             nan     0.1000    0.0240
     4        1.0924             nan     0.1000    0.0218
     5        1.0554             nan     0.1000    0.0185
     6        1.0220             nan     0.1000    0.0173
     7        0.9935             nan     0.1000    0.0125
     8        0.9718             nan     0.1000    0.0110
     9        0.9519             nan     0.1000    0.0092
    10        0.9347             nan     0.1000    0.0080
    20        0.8223             nan     0.1000    0.0019
    40        0.7450             nan     0.1000   -0.0006
    60        0.7005             nan     0.1000    0.0003
    80        0.6679             nan     0.1000   -0.0008
   100        0.6393             nan     0.1000   -0.0012
   120        0.6187             nan     0.1000   -0.0013
   140        0.5974             nan     0.1000   -0.0012
   160        0.5777             nan     0.1000   -0.0012
   180        0.5630             nan     0.1000   -0.0010
   200        0.5491             nan     0.1000   -0.0015
   220        0.5357             nan     0.1000   -0.0012
   240        0.5201             nan     0.1000   -0.0008
   260        0.5086             nan     0.1000   -0.0012
   280        0.4967             nan     0.1000   -0.0012
   300        0.4869             nan     0.1000   -0.0016
   320        0.4757             nan     0.1000   -0.0014
   340        0.4628             nan     0.1000   -0.0014
   360        0.4545             nan     0.1000   -0.0009
   380        0.4450             nan     0.1000   -0.0013
   400        0.4367             nan     0.1000   -0.0014
   420        0.4286             nan     0.1000   -0.0004
   440        0.4208             nan     0.1000   -0.0010
   460        0.4132             nan     0.1000   -0.0018
   480        0.4072             nan     0.1000   -0.0011
   500        0.3987             nan     0.1000   -0.0018
   520        0.3898             nan     0.1000   -0.0006
   540        0.3822             nan     0.1000   -0.0010
   560        0.3750             nan     0.1000   -0.0020
   580        0.3681             nan     0.1000   -0.0007
   600        0.3611             nan     0.1000   -0.0006
   620        0.3546             nan     0.1000   -0.0006
   640        0.3497             nan     0.1000   -0.0008
   660        0.3442             nan     0.1000   -0.0008
   680        0.3386             nan     0.1000   -0.0007
   700        0.3329             nan     0.1000   -0.0011
   720        0.3274             nan     0.1000   -0.0005
   740        0.3229             nan     0.1000   -0.0015
   760        0.3184             nan     0.1000   -0.0008
   780        0.3121             nan     0.1000   -0.0012
   800        0.3088             nan     0.1000   -0.0016
   820        0.3033             nan     0.1000   -0.0012
   840        0.2987             nan     0.1000   -0.0003
   860        0.2943             nan     0.1000   -0.0005
   880        0.2902             nan     0.1000   -0.0008
   900        0.2859             nan     0.1000   -0.0008
   920        0.2821             nan     0.1000   -0.0005
   940        0.2776             nan     0.1000   -0.0004
   960        0.2750             nan     0.1000   -0.0013
   980        0.2703             nan     0.1000   -0.0013
  1000        0.2671             nan     0.1000   -0.0010
  1020        0.2637             nan     0.1000   -0.0005
  1040        0.2606             nan     0.1000   -0.0007
  1060        0.2575             nan     0.1000   -0.0004
  1080        0.2536             nan     0.1000   -0.0008
  1100        0.2508             nan     0.1000   -0.0009

- Fold04.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3254             nan     0.0100    0.0028
     2        1.3197             nan     0.0100    0.0028
     3        1.3145             nan     0.0100    0.0028
     4        1.3091             nan     0.0100    0.0028
     5        1.3037             nan     0.0100    0.0026
     6        1.2986             nan     0.0100    0.0026
     7        1.2930             nan     0.0100    0.0025
     8        1.2884             nan     0.0100    0.0025
     9        1.2834             nan     0.0100    0.0025
    10        1.2786             nan     0.0100    0.0024
    20        1.2342             nan     0.0100    0.0020
    40        1.1670             nan     0.0100    0.0014
    60        1.1203             nan     0.0100    0.0010
    80        1.0834             nan     0.0100    0.0007
   100        1.0529             nan     0.0100    0.0006
   120        1.0282             nan     0.0100    0.0005
   140        1.0069             nan     0.0100    0.0003
   160        0.9888             nan     0.0100    0.0003
   180        0.9732             nan     0.0100    0.0002
   200        0.9601             nan     0.0100    0.0003
   220        0.9479             nan     0.0100    0.0002
   240        0.9375             nan     0.0100   -0.0000
   260        0.9278             nan     0.0100    0.0001
   280        0.9194             nan     0.0100    0.0000
   300        0.9114             nan     0.0100    0.0001
   320        0.9045             nan     0.0100    0.0001
   340        0.8989             nan     0.0100    0.0001
   360        0.8930             nan     0.0100    0.0001
   380        0.8878             nan     0.0100    0.0000
   400        0.8831             nan     0.0100    0.0001
   420        0.8782             nan     0.0100    0.0000
   440        0.8741             nan     0.0100    0.0001
   460        0.8697             nan     0.0100   -0.0000
   480        0.8653             nan     0.0100   -0.0000
   500        0.8610             nan     0.0100   -0.0000
   520        0.8574             nan     0.0100    0.0001
   540        0.8540             nan     0.0100    0.0000
   560        0.8506             nan     0.0100    0.0000
   580        0.8477             nan     0.0100   -0.0000
   600        0.8448             nan     0.0100   -0.0000
   620        0.8415             nan     0.0100   -0.0000
   640        0.8388             nan     0.0100    0.0000
   660        0.8360             nan     0.0100    0.0000
   680        0.8335             nan     0.0100   -0.0000
   700        0.8309             nan     0.0100    0.0000
   720        0.8284             nan     0.0100    0.0000
   740        0.8262             nan     0.0100    0.0000
   760        0.8240             nan     0.0100   -0.0000
   780        0.8218             nan     0.0100   -0.0001
   800        0.8194             nan     0.0100   -0.0001
   820        0.8175             nan     0.0100   -0.0002
   840        0.8154             nan     0.0100   -0.0000
   860        0.8134             nan     0.0100    0.0000
   880        0.8115             nan     0.0100   -0.0001
   900        0.8098             nan     0.0100   -0.0000
   920        0.8077             nan     0.0100   -0.0000
   940        0.8058             nan     0.0100   -0.0000
   960        0.8038             nan     0.0100   -0.0001
   980        0.8022             nan     0.0100   -0.0000
  1000        0.8008             nan     0.0100   -0.0000
  1020        0.7992             nan     0.0100   -0.0001
  1040        0.7976             nan     0.0100   -0.0000
  1060        0.7961             nan     0.0100   -0.0001
  1080        0.7946             nan     0.0100   -0.0000
  1100        0.7932             nan     0.0100   -0.0001

- Fold05.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0036
     2        1.3169             nan     0.0100    0.0034
     3        1.3099             nan     0.0100    0.0035
     4        1.3031             nan     0.0100    0.0033
     5        1.2966             nan     0.0100    0.0033
     6        1.2902             nan     0.0100    0.0030
     7        1.2839             nan     0.0100    0.0033
     8        1.2773             nan     0.0100    0.0033
     9        1.2712             nan     0.0100    0.0029
    10        1.2655             nan     0.0100    0.0027
    20        1.2094             nan     0.0100    0.0026
    40        1.1232             nan     0.0100    0.0018
    60        1.0598             nan     0.0100    0.0011
    80        1.0124             nan     0.0100    0.0010
   100        0.9753             nan     0.0100    0.0005
   120        0.9470             nan     0.0100    0.0005
   140        0.9236             nan     0.0100    0.0003
   160        0.9045             nan     0.0100    0.0002
   180        0.8884             nan     0.0100    0.0003
   200        0.8754             nan     0.0100    0.0001
   220        0.8636             nan     0.0100    0.0001
   240        0.8536             nan     0.0100    0.0001
   260        0.8445             nan     0.0100    0.0000
   280        0.8349             nan     0.0100    0.0002
   300        0.8272             nan     0.0100    0.0001
   320        0.8206             nan     0.0100   -0.0001
   340        0.8142             nan     0.0100    0.0001
   360        0.8079             nan     0.0100    0.0001
   380        0.8026             nan     0.0100    0.0001
   400        0.7975             nan     0.0100    0.0000
   420        0.7927             nan     0.0100   -0.0001
   440        0.7883             nan     0.0100   -0.0000
   460        0.7843             nan     0.0100    0.0000
   480        0.7802             nan     0.0100   -0.0000
   500        0.7760             nan     0.0100   -0.0001
   520        0.7727             nan     0.0100   -0.0000
   540        0.7693             nan     0.0100   -0.0001
   560        0.7658             nan     0.0100   -0.0000
   580        0.7623             nan     0.0100   -0.0000
   600        0.7589             nan     0.0100   -0.0001
   620        0.7559             nan     0.0100   -0.0001
   640        0.7530             nan     0.0100   -0.0000
   660        0.7503             nan     0.0100   -0.0001
   680        0.7473             nan     0.0100   -0.0000
   700        0.7443             nan     0.0100   -0.0001
   720        0.7415             nan     0.0100   -0.0000
   740        0.7394             nan     0.0100   -0.0001
   760        0.7365             nan     0.0100   -0.0002
   780        0.7337             nan     0.0100   -0.0001
   800        0.7308             nan     0.0100   -0.0000
   820        0.7283             nan     0.0100   -0.0002
   840        0.7258             nan     0.0100   -0.0001
   860        0.7238             nan     0.0100   -0.0001
   880        0.7217             nan     0.0100   -0.0000
   900        0.7196             nan     0.0100   -0.0001
   920        0.7173             nan     0.0100   -0.0001
   940        0.7151             nan     0.0100   -0.0001
   960        0.7134             nan     0.0100   -0.0000
   980        0.7112             nan     0.0100   -0.0001
  1000        0.7094             nan     0.0100   -0.0001
  1020        0.7072             nan     0.0100   -0.0001
  1040        0.7053             nan     0.0100   -0.0001
  1060        0.7038             nan     0.0100   -0.0000
  1080        0.7022             nan     0.0100   -0.0000
  1100        0.7005             nan     0.0100   -0.0000

- Fold05.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0039
     2        1.3143             nan     0.0100    0.0033
     3        1.3068             nan     0.0100    0.0038
     4        1.2992             nan     0.0100    0.0036
     5        1.2915             nan     0.0100    0.0041
     6        1.2841             nan     0.0100    0.0035
     7        1.2771             nan     0.0100    0.0035
     8        1.2697             nan     0.0100    0.0033
     9        1.2626             nan     0.0100    0.0034
    10        1.2559             nan     0.0100    0.0032
    20        1.1942             nan     0.0100    0.0025
    40        1.0995             nan     0.0100    0.0020
    60        1.0289             nan     0.0100    0.0013
    80        0.9777             nan     0.0100    0.0009
   100        0.9371             nan     0.0100    0.0009
   120        0.9067             nan     0.0100    0.0006
   140        0.8827             nan     0.0100    0.0005
   160        0.8625             nan     0.0100    0.0003
   180        0.8466             nan     0.0100    0.0002
   200        0.8325             nan     0.0100    0.0001
   220        0.8212             nan     0.0100    0.0001
   240        0.8105             nan     0.0100   -0.0001
   260        0.8005             nan     0.0100    0.0001
   280        0.7917             nan     0.0100    0.0001
   300        0.7837             nan     0.0100   -0.0002
   320        0.7764             nan     0.0100    0.0000
   340        0.7701             nan     0.0100   -0.0001
   360        0.7641             nan     0.0100   -0.0001
   380        0.7581             nan     0.0100   -0.0000
   400        0.7525             nan     0.0100   -0.0000
   420        0.7470             nan     0.0100    0.0001
   440        0.7416             nan     0.0100   -0.0001
   460        0.7374             nan     0.0100   -0.0001
   480        0.7328             nan     0.0100   -0.0000
   500        0.7281             nan     0.0100   -0.0000
   520        0.7240             nan     0.0100   -0.0001
   540        0.7195             nan     0.0100   -0.0001
   560        0.7157             nan     0.0100    0.0000
   580        0.7120             nan     0.0100   -0.0001
   600        0.7084             nan     0.0100   -0.0001
   620        0.7043             nan     0.0100   -0.0003
   640        0.7003             nan     0.0100    0.0000
   660        0.6972             nan     0.0100   -0.0002
   680        0.6940             nan     0.0100   -0.0002
   700        0.6908             nan     0.0100   -0.0000
   720        0.6872             nan     0.0100   -0.0001
   740        0.6845             nan     0.0100   -0.0000
   760        0.6816             nan     0.0100   -0.0002
   780        0.6792             nan     0.0100   -0.0000
   800        0.6760             nan     0.0100   -0.0000
   820        0.6731             nan     0.0100   -0.0001
   840        0.6707             nan     0.0100   -0.0002
   860        0.6678             nan     0.0100   -0.0000
   880        0.6650             nan     0.0100   -0.0001
   900        0.6624             nan     0.0100   -0.0001
   920        0.6596             nan     0.0100   -0.0001
   940        0.6576             nan     0.0100   -0.0001
   960        0.6546             nan     0.0100   -0.0001
   980        0.6519             nan     0.0100   -0.0002
  1000        0.6494             nan     0.0100   -0.0000
  1020        0.6469             nan     0.0100   -0.0000
  1040        0.6443             nan     0.0100   -0.0001
  1060        0.6414             nan     0.0100   -0.0001
  1080        0.6389             nan     0.0100   -0.0001
  1100        0.6365             nan     0.0100   -0.0001

- Fold05.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2759             nan     0.1000    0.0269
     2        1.2310             nan     0.1000    0.0210
     3        1.1922             nan     0.1000    0.0185
     4        1.1619             nan     0.1000    0.0144
     5        1.1336             nan     0.1000    0.0125
     6        1.1114             nan     0.1000    0.0102
     7        1.0939             nan     0.1000    0.0083
     8        1.0791             nan     0.1000    0.0064
     9        1.0594             nan     0.1000    0.0078
    10        1.0436             nan     0.1000    0.0070
    20        0.9526             nan     0.1000    0.0029
    40        0.8776             nan     0.1000    0.0007
    60        0.8387             nan     0.1000   -0.0005
    80        0.8164             nan     0.1000   -0.0007
   100        0.7992             nan     0.1000   -0.0010
   120        0.7859             nan     0.1000   -0.0010
   140        0.7757             nan     0.1000    0.0000
   160        0.7654             nan     0.1000   -0.0001
   180        0.7580             nan     0.1000   -0.0007
   200        0.7524             nan     0.1000   -0.0005
   220        0.7439             nan     0.1000   -0.0012
   240        0.7387             nan     0.1000   -0.0008
   260        0.7330             nan     0.1000   -0.0012
   280        0.7301             nan     0.1000   -0.0006
   300        0.7267             nan     0.1000   -0.0004
   320        0.7220             nan     0.1000   -0.0005
   340        0.7186             nan     0.1000   -0.0010
   360        0.7170             nan     0.1000   -0.0009
   380        0.7128             nan     0.1000   -0.0004
   400        0.7095             nan     0.1000   -0.0009
   420        0.7080             nan     0.1000   -0.0009
   440        0.7058             nan     0.1000   -0.0009
   460        0.7031             nan     0.1000   -0.0007
   480        0.7015             nan     0.1000   -0.0007
   500        0.6992             nan     0.1000   -0.0004
   520        0.6977             nan     0.1000   -0.0006
   540        0.6949             nan     0.1000   -0.0007
   560        0.6930             nan     0.1000   -0.0007
   580        0.6909             nan     0.1000   -0.0009
   600        0.6881             nan     0.1000   -0.0004
   620        0.6869             nan     0.1000   -0.0008
   640        0.6845             nan     0.1000   -0.0006
   660        0.6816             nan     0.1000   -0.0010
   680        0.6787             nan     0.1000   -0.0008
   700        0.6768             nan     0.1000   -0.0007
   720        0.6754             nan     0.1000   -0.0006
   740        0.6731             nan     0.1000   -0.0007
   760        0.6717             nan     0.1000   -0.0007
   780        0.6700             nan     0.1000   -0.0008
   800        0.6676             nan     0.1000   -0.0007
   820        0.6668             nan     0.1000   -0.0012
   840        0.6635             nan     0.1000   -0.0007
   860        0.6612             nan     0.1000   -0.0006
   880        0.6614             nan     0.1000   -0.0006
   900        0.6597             nan     0.1000   -0.0007
   920        0.6567             nan     0.1000   -0.0012
   940        0.6557             nan     0.1000   -0.0009
   960        0.6541             nan     0.1000   -0.0007
   980        0.6518             nan     0.1000   -0.0009
  1000        0.6497             nan     0.1000   -0.0004
  1020        0.6484             nan     0.1000   -0.0005
  1040        0.6476             nan     0.1000   -0.0008
  1060        0.6456             nan     0.1000   -0.0006
  1080        0.6446             nan     0.1000   -0.0008
  1100        0.6419             nan     0.1000   -0.0006

- Fold05.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2608             nan     0.1000    0.0345
     2        1.2007             nan     0.1000    0.0278
     3        1.1551             nan     0.1000    0.0216
     4        1.1142             nan     0.1000    0.0209
     5        1.0817             nan     0.1000    0.0169
     6        1.0516             nan     0.1000    0.0143
     7        1.0255             nan     0.1000    0.0117
     8        1.0009             nan     0.1000    0.0085
     9        0.9819             nan     0.1000    0.0095
    10        0.9654             nan     0.1000    0.0075
    20        0.8778             nan     0.1000    0.0021
    40        0.7967             nan     0.1000    0.0004
    60        0.7605             nan     0.1000   -0.0020
    80        0.7342             nan     0.1000   -0.0005
   100        0.7139             nan     0.1000   -0.0009
   120        0.6956             nan     0.1000   -0.0004
   140        0.6757             nan     0.1000   -0.0009
   160        0.6574             nan     0.1000   -0.0008
   180        0.6433             nan     0.1000   -0.0008
   200        0.6304             nan     0.1000   -0.0012
   220        0.6194             nan     0.1000   -0.0012
   240        0.6115             nan     0.1000   -0.0009
   260        0.6026             nan     0.1000   -0.0003
   280        0.5939             nan     0.1000   -0.0007
   300        0.5844             nan     0.1000   -0.0017
   320        0.5769             nan     0.1000   -0.0003
   340        0.5698             nan     0.1000   -0.0011
   360        0.5598             nan     0.1000   -0.0021
   380        0.5524             nan     0.1000   -0.0013
   400        0.5411             nan     0.1000   -0.0000
   420        0.5362             nan     0.1000   -0.0009
   440        0.5292             nan     0.1000   -0.0026
   460        0.5238             nan     0.1000   -0.0016
   480        0.5169             nan     0.1000   -0.0006
   500        0.5105             nan     0.1000   -0.0013
   520        0.5059             nan     0.1000   -0.0008
   540        0.4992             nan     0.1000   -0.0013
   560        0.4920             nan     0.1000   -0.0008
   580        0.4874             nan     0.1000   -0.0016
   600        0.4813             nan     0.1000   -0.0009
   620        0.4745             nan     0.1000   -0.0010
   640        0.4665             nan     0.1000   -0.0012
   660        0.4597             nan     0.1000   -0.0010
   680        0.4558             nan     0.1000   -0.0005
   700        0.4513             nan     0.1000   -0.0004
   720        0.4459             nan     0.1000   -0.0006
   740        0.4420             nan     0.1000   -0.0007
   760        0.4389             nan     0.1000   -0.0009
   780        0.4340             nan     0.1000   -0.0015
   800        0.4304             nan     0.1000   -0.0007
   820        0.4259             nan     0.1000   -0.0003
   840        0.4226             nan     0.1000   -0.0001
   860        0.4174             nan     0.1000   -0.0004
   880        0.4134             nan     0.1000   -0.0012
   900        0.4096             nan     0.1000   -0.0008
   920        0.4072             nan     0.1000   -0.0007
   940        0.4022             nan     0.1000   -0.0008
   960        0.3992             nan     0.1000   -0.0010
   980        0.3965             nan     0.1000   -0.0007
  1000        0.3923             nan     0.1000   -0.0006
  1020        0.3904             nan     0.1000   -0.0007
  1040        0.3880             nan     0.1000   -0.0011
  1060        0.3854             nan     0.1000   -0.0010
  1080        0.3814             nan     0.1000   -0.0009
  1100        0.3777             nan     0.1000   -0.0012

- Fold05.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2552             nan     0.1000    0.0337
     2        1.1903             nan     0.1000    0.0303
     3        1.1356             nan     0.1000    0.0253
     4        1.0917             nan     0.1000    0.0227
     5        1.0573             nan     0.1000    0.0171
     6        1.0233             nan     0.1000    0.0164
     7        0.9950             nan     0.1000    0.0123
     8        0.9684             nan     0.1000    0.0099
     9        0.9469             nan     0.1000    0.0089
    10        0.9275             nan     0.1000    0.0067
    20        0.8306             nan     0.1000    0.0013
    40        0.7533             nan     0.1000   -0.0010
    60        0.7097             nan     0.1000   -0.0009
    80        0.6829             nan     0.1000   -0.0004
   100        0.6575             nan     0.1000   -0.0010
   120        0.6346             nan     0.1000   -0.0017
   140        0.6144             nan     0.1000   -0.0014
   160        0.5922             nan     0.1000   -0.0003
   180        0.5724             nan     0.1000   -0.0004
   200        0.5578             nan     0.1000   -0.0020
   220        0.5458             nan     0.1000   -0.0021
   240        0.5266             nan     0.1000   -0.0009
   260        0.5126             nan     0.1000   -0.0019
   280        0.4998             nan     0.1000   -0.0007
   300        0.4897             nan     0.1000   -0.0011
   320        0.4780             nan     0.1000   -0.0004
   340        0.4681             nan     0.1000   -0.0008
   360        0.4582             nan     0.1000   -0.0005
   380        0.4504             nan     0.1000   -0.0008
   400        0.4429             nan     0.1000   -0.0009
   420        0.4344             nan     0.1000   -0.0008
   440        0.4254             nan     0.1000   -0.0011
   460        0.4179             nan     0.1000   -0.0010
   480        0.4102             nan     0.1000   -0.0009
   500        0.4009             nan     0.1000   -0.0007
   520        0.3929             nan     0.1000   -0.0007
   540        0.3861             nan     0.1000   -0.0009
   560        0.3766             nan     0.1000   -0.0008
   580        0.3691             nan     0.1000   -0.0011
   600        0.3627             nan     0.1000   -0.0007
   620        0.3565             nan     0.1000   -0.0008
   640        0.3499             nan     0.1000   -0.0005
   660        0.3439             nan     0.1000   -0.0010
   680        0.3402             nan     0.1000   -0.0009
   700        0.3353             nan     0.1000   -0.0009
   720        0.3289             nan     0.1000   -0.0013
   740        0.3229             nan     0.1000   -0.0008
   760        0.3176             nan     0.1000   -0.0013
   780        0.3131             nan     0.1000   -0.0011
   800        0.3078             nan     0.1000   -0.0005
   820        0.3034             nan     0.1000   -0.0012
   840        0.2991             nan     0.1000   -0.0004
   860        0.2933             nan     0.1000   -0.0005
   880        0.2888             nan     0.1000   -0.0013
   900        0.2818             nan     0.1000   -0.0011
   920        0.2781             nan     0.1000   -0.0006
   940        0.2756             nan     0.1000   -0.0012
   960        0.2711             nan     0.1000   -0.0006
   980        0.2675             nan     0.1000   -0.0006
  1000        0.2630             nan     0.1000   -0.0008
  1020        0.2600             nan     0.1000   -0.0010
  1040        0.2564             nan     0.1000   -0.0005
  1060        0.2519             nan     0.1000   -0.0012
  1080        0.2480             nan     0.1000   -0.0004
  1100        0.2444             nan     0.1000   -0.0006

- Fold05.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3269             nan     0.0100    0.0028
     2        1.3215             nan     0.0100    0.0027
     3        1.3154             nan     0.0100    0.0026
     4        1.3104             nan     0.0100    0.0026
     5        1.3052             nan     0.0100    0.0025
     6        1.2999             nan     0.0100    0.0025
     7        1.2946             nan     0.0100    0.0025
     8        1.2903             nan     0.0100    0.0024
     9        1.2858             nan     0.0100    0.0024
    10        1.2814             nan     0.0100    0.0023
    20        1.2397             nan     0.0100    0.0019
    40        1.1751             nan     0.0100    0.0008
    60        1.1297             nan     0.0100    0.0009
    80        1.0932             nan     0.0100    0.0008
   100        1.0648             nan     0.0100    0.0006
   120        1.0417             nan     0.0100    0.0005
   140        1.0207             nan     0.0100    0.0003
   160        1.0032             nan     0.0100    0.0003
   180        0.9882             nan     0.0100    0.0003
   200        0.9738             nan     0.0100    0.0002
   220        0.9616             nan     0.0100    0.0002
   240        0.9509             nan     0.0100    0.0002
   260        0.9413             nan     0.0100    0.0001
   280        0.9329             nan     0.0100    0.0000
   300        0.9245             nan     0.0100    0.0001
   320        0.9172             nan     0.0100    0.0001
   340        0.9103             nan     0.0100    0.0001
   360        0.9041             nan     0.0100    0.0001
   380        0.8984             nan     0.0100    0.0001
   400        0.8929             nan     0.0100   -0.0000
   420        0.8882             nan     0.0100   -0.0000
   440        0.8834             nan     0.0100   -0.0000
   460        0.8787             nan     0.0100    0.0000
   480        0.8742             nan     0.0100    0.0000
   500        0.8704             nan     0.0100    0.0000
   520        0.8665             nan     0.0100    0.0000
   540        0.8629             nan     0.0100    0.0000
   560        0.8596             nan     0.0100    0.0001
   580        0.8564             nan     0.0100    0.0000
   600        0.8538             nan     0.0100    0.0000
   620        0.8509             nan     0.0100   -0.0000
   640        0.8480             nan     0.0100    0.0000
   660        0.8449             nan     0.0100    0.0000
   680        0.8421             nan     0.0100   -0.0001
   700        0.8393             nan     0.0100    0.0000
   720        0.8369             nan     0.0100   -0.0001
   740        0.8345             nan     0.0100   -0.0001
   760        0.8321             nan     0.0100   -0.0000
   780        0.8299             nan     0.0100   -0.0001
   800        0.8277             nan     0.0100   -0.0000
   820        0.8254             nan     0.0100   -0.0000
   840        0.8237             nan     0.0100   -0.0001
   860        0.8216             nan     0.0100    0.0000
   880        0.8198             nan     0.0100   -0.0000
   900        0.8178             nan     0.0100   -0.0000
   920        0.8160             nan     0.0100   -0.0000
   940        0.8146             nan     0.0100   -0.0001
   960        0.8131             nan     0.0100   -0.0000
   980        0.8116             nan     0.0100   -0.0001
  1000        0.8099             nan     0.0100    0.0000
  1020        0.8082             nan     0.0100    0.0000
  1040        0.8065             nan     0.0100    0.0000
  1060        0.8050             nan     0.0100   -0.0002
  1080        0.8038             nan     0.0100   -0.0001
  1100        0.8025             nan     0.0100   -0.0000

- Fold06.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3245             nan     0.0100    0.0035
     2        1.3175             nan     0.0100    0.0034
     3        1.3112             nan     0.0100    0.0032
     4        1.3047             nan     0.0100    0.0031
     5        1.2981             nan     0.0100    0.0034
     6        1.2922             nan     0.0100    0.0033
     7        1.2861             nan     0.0100    0.0031
     8        1.2799             nan     0.0100    0.0030
     9        1.2740             nan     0.0100    0.0029
    10        1.2679             nan     0.0100    0.0032
    20        1.2149             nan     0.0100    0.0026
    40        1.1317             nan     0.0100    0.0016
    60        1.0695             nan     0.0100    0.0013
    80        1.0222             nan     0.0100    0.0010
   100        0.9865             nan     0.0100    0.0008
   120        0.9597             nan     0.0100    0.0006
   140        0.9377             nan     0.0100    0.0004
   160        0.9205             nan     0.0100    0.0001
   180        0.9044             nan     0.0100    0.0002
   200        0.8902             nan     0.0100    0.0002
   220        0.8777             nan     0.0100    0.0002
   240        0.8689             nan     0.0100   -0.0000
   260        0.8597             nan     0.0100    0.0001
   280        0.8518             nan     0.0100    0.0001
   300        0.8444             nan     0.0100    0.0001
   320        0.8379             nan     0.0100    0.0001
   340        0.8306             nan     0.0100    0.0001
   360        0.8241             nan     0.0100    0.0000
   380        0.8181             nan     0.0100   -0.0001
   400        0.8132             nan     0.0100   -0.0000
   420        0.8089             nan     0.0100   -0.0000
   440        0.8049             nan     0.0100   -0.0000
   460        0.8003             nan     0.0100   -0.0000
   480        0.7957             nan     0.0100   -0.0000
   500        0.7918             nan     0.0100   -0.0001
   520        0.7875             nan     0.0100   -0.0000
   540        0.7843             nan     0.0100   -0.0001
   560        0.7810             nan     0.0100   -0.0001
   580        0.7774             nan     0.0100    0.0000
   600        0.7738             nan     0.0100    0.0000
   620        0.7710             nan     0.0100   -0.0002
   640        0.7683             nan     0.0100   -0.0000
   660        0.7653             nan     0.0100   -0.0001
   680        0.7625             nan     0.0100   -0.0001
   700        0.7602             nan     0.0100   -0.0001
   720        0.7571             nan     0.0100   -0.0000
   740        0.7544             nan     0.0100    0.0000
   760        0.7523             nan     0.0100   -0.0001
   780        0.7500             nan     0.0100   -0.0000
   800        0.7472             nan     0.0100   -0.0001
   820        0.7447             nan     0.0100   -0.0001
   840        0.7421             nan     0.0100   -0.0001
   860        0.7399             nan     0.0100   -0.0001
   880        0.7377             nan     0.0100   -0.0000
   900        0.7357             nan     0.0100   -0.0001
   920        0.7337             nan     0.0100   -0.0001
   940        0.7316             nan     0.0100   -0.0001
   960        0.7289             nan     0.0100    0.0000
   980        0.7270             nan     0.0100   -0.0002
  1000        0.7253             nan     0.0100   -0.0000
  1020        0.7231             nan     0.0100   -0.0001
  1040        0.7212             nan     0.0100   -0.0002
  1060        0.7192             nan     0.0100   -0.0000
  1080        0.7170             nan     0.0100   -0.0001
  1100        0.7152             nan     0.0100   -0.0001

- Fold06.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0033
     2        1.3169             nan     0.0100    0.0037
     3        1.3095             nan     0.0100    0.0036
     4        1.3018             nan     0.0100    0.0037
     5        1.2943             nan     0.0100    0.0036
     6        1.2872             nan     0.0100    0.0035
     7        1.2801             nan     0.0100    0.0035
     8        1.2732             nan     0.0100    0.0033
     9        1.2663             nan     0.0100    0.0033
    10        1.2597             nan     0.0100    0.0033
    20        1.1988             nan     0.0100    0.0026
    40        1.1064             nan     0.0100    0.0018
    60        1.0376             nan     0.0100    0.0013
    80        0.9874             nan     0.0100    0.0009
   100        0.9492             nan     0.0100    0.0008
   120        0.9211             nan     0.0100    0.0003
   140        0.8977             nan     0.0100    0.0006
   160        0.8778             nan     0.0100    0.0002
   180        0.8619             nan     0.0100    0.0001
   200        0.8488             nan     0.0100    0.0000
   220        0.8369             nan     0.0100   -0.0000
   240        0.8257             nan     0.0100   -0.0001
   260        0.8163             nan     0.0100    0.0001
   280        0.8077             nan     0.0100    0.0001
   300        0.8002             nan     0.0100   -0.0000
   320        0.7928             nan     0.0100    0.0001
   340        0.7856             nan     0.0100    0.0000
   360        0.7790             nan     0.0100   -0.0001
   380        0.7725             nan     0.0100   -0.0002
   400        0.7667             nan     0.0100    0.0000
   420        0.7613             nan     0.0100    0.0000
   440        0.7562             nan     0.0100   -0.0000
   460        0.7518             nan     0.0100   -0.0000
   480        0.7466             nan     0.0100   -0.0001
   500        0.7423             nan     0.0100   -0.0001
   520        0.7379             nan     0.0100    0.0000
   540        0.7340             nan     0.0100   -0.0001
   560        0.7298             nan     0.0100    0.0000
   580        0.7261             nan     0.0100   -0.0001
   600        0.7226             nan     0.0100   -0.0001
   620        0.7188             nan     0.0100   -0.0000
   640        0.7148             nan     0.0100    0.0001
   660        0.7108             nan     0.0100   -0.0001
   680        0.7077             nan     0.0100   -0.0002
   700        0.7038             nan     0.0100   -0.0001
   720        0.7006             nan     0.0100   -0.0001
   740        0.6978             nan     0.0100   -0.0001
   760        0.6947             nan     0.0100   -0.0000
   780        0.6917             nan     0.0100   -0.0001
   800        0.6886             nan     0.0100   -0.0000
   820        0.6860             nan     0.0100   -0.0001
   840        0.6830             nan     0.0100   -0.0001
   860        0.6805             nan     0.0100   -0.0001
   880        0.6779             nan     0.0100   -0.0001
   900        0.6754             nan     0.0100   -0.0002
   920        0.6727             nan     0.0100   -0.0001
   940        0.6699             nan     0.0100   -0.0001
   960        0.6672             nan     0.0100   -0.0000
   980        0.6652             nan     0.0100   -0.0001
  1000        0.6626             nan     0.0100   -0.0001
  1020        0.6603             nan     0.0100   -0.0001
  1040        0.6582             nan     0.0100   -0.0001
  1060        0.6559             nan     0.0100   -0.0002
  1080        0.6531             nan     0.0100   -0.0002
  1100        0.6506             nan     0.0100   -0.0001

- Fold06.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2800             nan     0.1000    0.0260
     2        1.2347             nan     0.1000    0.0213
     3        1.2020             nan     0.1000    0.0174
     4        1.1721             nan     0.1000    0.0158
     5        1.1447             nan     0.1000    0.0111
     6        1.1230             nan     0.1000    0.0103
     7        1.1066             nan     0.1000    0.0082
     8        1.0889             nan     0.1000    0.0076
     9        1.0759             nan     0.1000    0.0065
    10        1.0623             nan     0.1000    0.0062
    20        0.9697             nan     0.1000    0.0033
    40        0.8932             nan     0.1000    0.0003
    60        0.8530             nan     0.1000   -0.0005
    80        0.8274             nan     0.1000   -0.0002
   100        0.8105             nan     0.1000   -0.0007
   120        0.7961             nan     0.1000   -0.0004
   140        0.7861             nan     0.1000   -0.0012
   160        0.7769             nan     0.1000   -0.0016
   180        0.7680             nan     0.1000   -0.0000
   200        0.7616             nan     0.1000   -0.0001
   220        0.7551             nan     0.1000   -0.0002
   240        0.7486             nan     0.1000   -0.0007
   260        0.7422             nan     0.1000   -0.0008
   280        0.7371             nan     0.1000   -0.0005
   300        0.7333             nan     0.1000   -0.0005
   320        0.7289             nan     0.1000   -0.0007
   340        0.7259             nan     0.1000   -0.0005
   360        0.7235             nan     0.1000   -0.0011
   380        0.7202             nan     0.1000   -0.0008
   400        0.7164             nan     0.1000   -0.0008
   420        0.7122             nan     0.1000   -0.0001
   440        0.7104             nan     0.1000   -0.0009
   460        0.7086             nan     0.1000   -0.0013
   480        0.7064             nan     0.1000   -0.0015
   500        0.7026             nan     0.1000   -0.0008
   520        0.7010             nan     0.1000   -0.0007
   540        0.6991             nan     0.1000   -0.0009
   560        0.6970             nan     0.1000   -0.0010
   580        0.6936             nan     0.1000   -0.0011
   600        0.6925             nan     0.1000   -0.0010
   620        0.6893             nan     0.1000   -0.0011
   640        0.6878             nan     0.1000   -0.0010
   660        0.6851             nan     0.1000   -0.0005
   680        0.6834             nan     0.1000   -0.0009
   700        0.6827             nan     0.1000   -0.0005
   720        0.6797             nan     0.1000   -0.0018
   740        0.6784             nan     0.1000   -0.0016
   760        0.6756             nan     0.1000   -0.0003
   780        0.6745             nan     0.1000   -0.0007
   800        0.6727             nan     0.1000   -0.0010
   820        0.6713             nan     0.1000   -0.0010
   840        0.6709             nan     0.1000   -0.0003
   860        0.6686             nan     0.1000   -0.0016
   880        0.6676             nan     0.1000   -0.0003
   900        0.6659             nan     0.1000   -0.0003
   920        0.6638             nan     0.1000   -0.0007
   940        0.6630             nan     0.1000   -0.0007
   960        0.6606             nan     0.1000   -0.0006
   980        0.6591             nan     0.1000   -0.0005
  1000        0.6577             nan     0.1000   -0.0008
  1020        0.6560             nan     0.1000   -0.0006
  1040        0.6544             nan     0.1000   -0.0007
  1060        0.6535             nan     0.1000   -0.0014
  1080        0.6526             nan     0.1000   -0.0003
  1100        0.6518             nan     0.1000   -0.0004

- Fold06.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2645             nan     0.1000    0.0335
     2        1.2041             nan     0.1000    0.0270
     3        1.1587             nan     0.1000    0.0222
     4        1.1173             nan     0.1000    0.0192
     5        1.0863             nan     0.1000    0.0153
     6        1.0561             nan     0.1000    0.0126
     7        1.0340             nan     0.1000    0.0105
     8        1.0100             nan     0.1000    0.0102
     9        0.9927             nan     0.1000    0.0088
    10        0.9750             nan     0.1000    0.0070
    20        0.8821             nan     0.1000    0.0009
    40        0.8152             nan     0.1000   -0.0003
    60        0.7761             nan     0.1000   -0.0007
    80        0.7518             nan     0.1000   -0.0005
   100        0.7313             nan     0.1000   -0.0005
   120        0.7120             nan     0.1000   -0.0007
   140        0.6986             nan     0.1000   -0.0013
   160        0.6801             nan     0.1000   -0.0009
   180        0.6648             nan     0.1000   -0.0006
   200        0.6506             nan     0.1000   -0.0008
   220        0.6398             nan     0.1000   -0.0003
   240        0.6269             nan     0.1000   -0.0008
   260        0.6179             nan     0.1000   -0.0011
   280        0.6071             nan     0.1000   -0.0012
   300        0.5969             nan     0.1000   -0.0002
   320        0.5894             nan     0.1000   -0.0008
   340        0.5802             nan     0.1000   -0.0007
   360        0.5730             nan     0.1000   -0.0015
   380        0.5642             nan     0.1000   -0.0010
   400        0.5580             nan     0.1000   -0.0012
   420        0.5515             nan     0.1000   -0.0008
   440        0.5413             nan     0.1000   -0.0013
   460        0.5344             nan     0.1000   -0.0011
   480        0.5296             nan     0.1000   -0.0009
   500        0.5250             nan     0.1000   -0.0006
   520        0.5185             nan     0.1000   -0.0010
   540        0.5126             nan     0.1000   -0.0016
   560        0.5071             nan     0.1000   -0.0000
   580        0.5023             nan     0.1000   -0.0005
   600        0.4977             nan     0.1000   -0.0008
   620        0.4927             nan     0.1000   -0.0010
   640        0.4880             nan     0.1000   -0.0005
   660        0.4820             nan     0.1000   -0.0014
   680        0.4796             nan     0.1000   -0.0003
   700        0.4755             nan     0.1000   -0.0008
   720        0.4712             nan     0.1000   -0.0010
   740        0.4656             nan     0.1000   -0.0007
   760        0.4622             nan     0.1000   -0.0011
   780        0.4577             nan     0.1000   -0.0011
   800        0.4530             nan     0.1000   -0.0007
   820        0.4495             nan     0.1000   -0.0008
   840        0.4445             nan     0.1000   -0.0011
   860        0.4420             nan     0.1000   -0.0005
   880        0.4384             nan     0.1000   -0.0006
   900        0.4330             nan     0.1000   -0.0013
   920        0.4303             nan     0.1000   -0.0006
   940        0.4271             nan     0.1000   -0.0008
   960        0.4239             nan     0.1000   -0.0007
   980        0.4198             nan     0.1000   -0.0006
  1000        0.4160             nan     0.1000   -0.0014
  1020        0.4130             nan     0.1000   -0.0003
  1040        0.4098             nan     0.1000   -0.0010
  1060        0.4071             nan     0.1000   -0.0012
  1080        0.4051             nan     0.1000   -0.0007
  1100        0.4024             nan     0.1000   -0.0004

- Fold06.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2571             nan     0.1000    0.0348
     2        1.1947             nan     0.1000    0.0302
     3        1.1399             nan     0.1000    0.0257
     4        1.0965             nan     0.1000    0.0204
     5        1.0607             nan     0.1000    0.0177
     6        1.0301             nan     0.1000    0.0155
     7        1.0028             nan     0.1000    0.0121
     8        0.9790             nan     0.1000    0.0083
     9        0.9626             nan     0.1000    0.0072
    10        0.9442             nan     0.1000    0.0070
    20        0.8490             nan     0.1000    0.0011
    40        0.7762             nan     0.1000    0.0004
    60        0.7279             nan     0.1000   -0.0007
    80        0.7030             nan     0.1000   -0.0007
   100        0.6770             nan     0.1000   -0.0014
   120        0.6516             nan     0.1000   -0.0022
   140        0.6289             nan     0.1000   -0.0021
   160        0.6130             nan     0.1000   -0.0014
   180        0.5954             nan     0.1000   -0.0009
   200        0.5764             nan     0.1000   -0.0008
   220        0.5635             nan     0.1000   -0.0024
   240        0.5482             nan     0.1000   -0.0012
   260        0.5331             nan     0.1000   -0.0007
   280        0.5173             nan     0.1000   -0.0009
   300        0.5053             nan     0.1000   -0.0019
   320        0.4937             nan     0.1000   -0.0009
   340        0.4857             nan     0.1000   -0.0011
   360        0.4758             nan     0.1000   -0.0015
   380        0.4642             nan     0.1000   -0.0008
   400        0.4570             nan     0.1000   -0.0009
   420        0.4478             nan     0.1000   -0.0009
   440        0.4392             nan     0.1000   -0.0008
   460        0.4309             nan     0.1000   -0.0009
   480        0.4232             nan     0.1000   -0.0012
   500        0.4154             nan     0.1000   -0.0015
   520        0.4075             nan     0.1000   -0.0008
   540        0.4000             nan     0.1000   -0.0006
   560        0.3945             nan     0.1000   -0.0011
   580        0.3877             nan     0.1000   -0.0015
   600        0.3826             nan     0.1000   -0.0013
   620        0.3796             nan     0.1000   -0.0008
   640        0.3704             nan     0.1000   -0.0008
   660        0.3642             nan     0.1000   -0.0006
   680        0.3576             nan     0.1000   -0.0009
   700        0.3536             nan     0.1000   -0.0010
   720        0.3490             nan     0.1000   -0.0009
   740        0.3442             nan     0.1000   -0.0014
   760        0.3372             nan     0.1000   -0.0008
   780        0.3333             nan     0.1000   -0.0010
   800        0.3275             nan     0.1000   -0.0005
   820        0.3233             nan     0.1000   -0.0009
   840        0.3204             nan     0.1000   -0.0010
   860        0.3153             nan     0.1000   -0.0006
   880        0.3107             nan     0.1000   -0.0011
   900        0.3077             nan     0.1000   -0.0009
   920        0.3024             nan     0.1000   -0.0006
   940        0.2957             nan     0.1000   -0.0005
   960        0.2911             nan     0.1000   -0.0005
   980        0.2878             nan     0.1000   -0.0006
  1000        0.2831             nan     0.1000   -0.0009
  1020        0.2810             nan     0.1000   -0.0006
  1040        0.2762             nan     0.1000   -0.0005
  1060        0.2725             nan     0.1000   -0.0008
  1080        0.2680             nan     0.1000   -0.0006
  1100        0.2649             nan     0.1000   -0.0007

- Fold06.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3257             nan     0.0100    0.0031
     2        1.3194             nan     0.0100    0.0030
     3        1.3133             nan     0.0100    0.0029
     4        1.3072             nan     0.0100    0.0028
     5        1.3015             nan     0.0100    0.0028
     6        1.2961             nan     0.0100    0.0028
     7        1.2904             nan     0.0100    0.0027
     8        1.2847             nan     0.0100    0.0026
     9        1.2797             nan     0.0100    0.0026
    10        1.2744             nan     0.0100    0.0025
    20        1.2277             nan     0.0100    0.0021
    40        1.1562             nan     0.0100    0.0014
    60        1.1051             nan     0.0100    0.0010
    80        1.0677             nan     0.0100    0.0008
   100        1.0370             nan     0.0100    0.0006
   120        1.0121             nan     0.0100    0.0005
   140        0.9914             nan     0.0100    0.0004
   160        0.9738             nan     0.0100    0.0002
   180        0.9581             nan     0.0100    0.0002
   200        0.9451             nan     0.0100    0.0002
   220        0.9341             nan     0.0100    0.0003
   240        0.9238             nan     0.0100    0.0002
   260        0.9148             nan     0.0100    0.0001
   280        0.9066             nan     0.0100    0.0002
   300        0.8993             nan     0.0100    0.0001
   320        0.8929             nan     0.0100    0.0001
   340        0.8869             nan     0.0100    0.0001
   360        0.8813             nan     0.0100    0.0001
   380        0.8765             nan     0.0100   -0.0000
   400        0.8713             nan     0.0100    0.0001
   420        0.8668             nan     0.0100    0.0000
   440        0.8623             nan     0.0100    0.0000
   460        0.8582             nan     0.0100   -0.0000
   480        0.8545             nan     0.0100    0.0000
   500        0.8503             nan     0.0100    0.0000
   520        0.8470             nan     0.0100    0.0000
   540        0.8436             nan     0.0100    0.0000
   560        0.8401             nan     0.0100    0.0000
   580        0.8371             nan     0.0100   -0.0001
   600        0.8338             nan     0.0100   -0.0000
   620        0.8306             nan     0.0100    0.0000
   640        0.8278             nan     0.0100   -0.0000
   660        0.8252             nan     0.0100   -0.0000
   680        0.8226             nan     0.0100   -0.0001
   700        0.8201             nan     0.0100    0.0000
   720        0.8179             nan     0.0100   -0.0001
   740        0.8156             nan     0.0100   -0.0000
   760        0.8135             nan     0.0100   -0.0001
   780        0.8114             nan     0.0100   -0.0000
   800        0.8092             nan     0.0100   -0.0000
   820        0.8073             nan     0.0100   -0.0000
   840        0.8054             nan     0.0100   -0.0000
   860        0.8033             nan     0.0100    0.0000
   880        0.8014             nan     0.0100   -0.0001
   900        0.7997             nan     0.0100   -0.0002
   920        0.7982             nan     0.0100   -0.0001
   940        0.7966             nan     0.0100   -0.0000
   960        0.7949             nan     0.0100   -0.0001
   980        0.7933             nan     0.0100   -0.0001
  1000        0.7918             nan     0.0100    0.0000
  1020        0.7905             nan     0.0100   -0.0001
  1040        0.7892             nan     0.0100   -0.0000
  1060        0.7880             nan     0.0100   -0.0001
  1080        0.7867             nan     0.0100   -0.0001
  1100        0.7857             nan     0.0100   -0.0001

- Fold07.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3246             nan     0.0100    0.0037
     2        1.3171             nan     0.0100    0.0036
     3        1.3099             nan     0.0100    0.0035
     4        1.3031             nan     0.0100    0.0037
     5        1.2966             nan     0.0100    0.0031
     6        1.2900             nan     0.0100    0.0034
     7        1.2832             nan     0.0100    0.0030
     8        1.2769             nan     0.0100    0.0033
     9        1.2707             nan     0.0100    0.0027
    10        1.2648             nan     0.0100    0.0030
    20        1.2084             nan     0.0100    0.0023
    40        1.1187             nan     0.0100    0.0018
    60        1.0545             nan     0.0100    0.0013
    80        1.0043             nan     0.0100    0.0010
   100        0.9669             nan     0.0100    0.0007
   120        0.9378             nan     0.0100    0.0005
   140        0.9153             nan     0.0100    0.0004
   160        0.8975             nan     0.0100    0.0003
   180        0.8816             nan     0.0100    0.0004
   200        0.8675             nan     0.0100    0.0000
   220        0.8555             nan     0.0100    0.0001
   240        0.8445             nan     0.0100    0.0000
   260        0.8356             nan     0.0100    0.0001
   280        0.8281             nan     0.0100   -0.0000
   300        0.8210             nan     0.0100    0.0001
   320        0.8138             nan     0.0100   -0.0000
   340        0.8079             nan     0.0100    0.0000
   360        0.8024             nan     0.0100    0.0000
   380        0.7965             nan     0.0100   -0.0000
   400        0.7909             nan     0.0100   -0.0000
   420        0.7858             nan     0.0100    0.0001
   440        0.7809             nan     0.0100    0.0000
   460        0.7765             nan     0.0100    0.0000
   480        0.7719             nan     0.0100   -0.0001
   500        0.7680             nan     0.0100    0.0000
   520        0.7638             nan     0.0100   -0.0000
   540        0.7606             nan     0.0100   -0.0000
   560        0.7575             nan     0.0100   -0.0000
   580        0.7540             nan     0.0100   -0.0001
   600        0.7508             nan     0.0100   -0.0000
   620        0.7480             nan     0.0100   -0.0001
   640        0.7454             nan     0.0100   -0.0001
   660        0.7421             nan     0.0100   -0.0000
   680        0.7393             nan     0.0100   -0.0001
   700        0.7362             nan     0.0100   -0.0001
   720        0.7339             nan     0.0100   -0.0001
   740        0.7316             nan     0.0100   -0.0001
   760        0.7292             nan     0.0100   -0.0001
   780        0.7269             nan     0.0100   -0.0001
   800        0.7243             nan     0.0100   -0.0001
   820        0.7221             nan     0.0100   -0.0001
   840        0.7197             nan     0.0100   -0.0001
   860        0.7176             nan     0.0100   -0.0001
   880        0.7157             nan     0.0100   -0.0001
   900        0.7132             nan     0.0100   -0.0000
   920        0.7110             nan     0.0100   -0.0000
   940        0.7089             nan     0.0100   -0.0001
   960        0.7067             nan     0.0100   -0.0001
   980        0.7049             nan     0.0100   -0.0002
  1000        0.7031             nan     0.0100   -0.0001
  1020        0.7011             nan     0.0100   -0.0001
  1040        0.6991             nan     0.0100   -0.0001
  1060        0.6969             nan     0.0100   -0.0001
  1080        0.6954             nan     0.0100   -0.0001
  1100        0.6931             nan     0.0100   -0.0001

- Fold07.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3236             nan     0.0100    0.0040
     2        1.3153             nan     0.0100    0.0037
     3        1.3074             nan     0.0100    0.0037
     4        1.2997             nan     0.0100    0.0038
     5        1.2920             nan     0.0100    0.0037
     6        1.2843             nan     0.0100    0.0035
     7        1.2768             nan     0.0100    0.0034
     8        1.2696             nan     0.0100    0.0035
     9        1.2623             nan     0.0100    0.0035
    10        1.2559             nan     0.0100    0.0033
    20        1.1933             nan     0.0100    0.0028
    40        1.0975             nan     0.0100    0.0019
    60        1.0275             nan     0.0100    0.0014
    80        0.9755             nan     0.0100    0.0011
   100        0.9350             nan     0.0100    0.0006
   120        0.9060             nan     0.0100    0.0006
   140        0.8818             nan     0.0100    0.0004
   160        0.8622             nan     0.0100    0.0002
   180        0.8452             nan     0.0100    0.0003
   200        0.8316             nan     0.0100    0.0000
   220        0.8193             nan     0.0100    0.0001
   240        0.8086             nan     0.0100    0.0001
   260        0.7991             nan     0.0100   -0.0000
   280        0.7908             nan     0.0100   -0.0001
   300        0.7821             nan     0.0100   -0.0001
   320        0.7749             nan     0.0100   -0.0000
   340        0.7685             nan     0.0100   -0.0001
   360        0.7625             nan     0.0100    0.0000
   380        0.7569             nan     0.0100    0.0001
   400        0.7504             nan     0.0100   -0.0000
   420        0.7450             nan     0.0100   -0.0001
   440        0.7402             nan     0.0100   -0.0000
   460        0.7356             nan     0.0100   -0.0000
   480        0.7312             nan     0.0100    0.0000
   500        0.7272             nan     0.0100   -0.0003
   520        0.7221             nan     0.0100    0.0000
   540        0.7177             nan     0.0100   -0.0002
   560        0.7138             nan     0.0100   -0.0001
   580        0.7098             nan     0.0100   -0.0001
   600        0.7064             nan     0.0100   -0.0000
   620        0.7024             nan     0.0100   -0.0002
   640        0.6988             nan     0.0100   -0.0002
   660        0.6954             nan     0.0100    0.0000
   680        0.6924             nan     0.0100   -0.0001
   700        0.6888             nan     0.0100   -0.0002
   720        0.6852             nan     0.0100    0.0000
   740        0.6821             nan     0.0100   -0.0000
   760        0.6786             nan     0.0100    0.0000
   780        0.6757             nan     0.0100   -0.0001
   800        0.6724             nan     0.0100   -0.0001
   820        0.6695             nan     0.0100   -0.0001
   840        0.6671             nan     0.0100   -0.0003
   860        0.6637             nan     0.0100   -0.0001
   880        0.6611             nan     0.0100   -0.0001
   900        0.6584             nan     0.0100   -0.0001
   920        0.6559             nan     0.0100   -0.0001
   940        0.6534             nan     0.0100   -0.0000
   960        0.6508             nan     0.0100   -0.0001
   980        0.6481             nan     0.0100   -0.0001
  1000        0.6455             nan     0.0100   -0.0001
  1020        0.6433             nan     0.0100   -0.0001
  1040        0.6411             nan     0.0100   -0.0002
  1060        0.6389             nan     0.0100   -0.0002
  1080        0.6366             nan     0.0100   -0.0001
  1100        0.6341             nan     0.0100   -0.0002

- Fold07.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2737             nan     0.1000    0.0288
     2        1.2198             nan     0.1000    0.0241
     3        1.1821             nan     0.1000    0.0190
     4        1.1486             nan     0.1000    0.0151
     5        1.1284             nan     0.1000    0.0071
     6        1.1023             nan     0.1000    0.0125
     7        1.0794             nan     0.1000    0.0103
     8        1.0637             nan     0.1000    0.0058
     9        1.0460             nan     0.1000    0.0082
    10        1.0317             nan     0.1000    0.0065
    20        0.9422             nan     0.1000    0.0011
    40        0.8725             nan     0.1000    0.0008
    60        0.8347             nan     0.1000   -0.0009
    80        0.8095             nan     0.1000   -0.0001
   100        0.7927             nan     0.1000   -0.0005
   120        0.7813             nan     0.1000   -0.0006
   140        0.7695             nan     0.1000   -0.0004
   160        0.7622             nan     0.1000   -0.0004
   180        0.7552             nan     0.1000   -0.0006
   200        0.7500             nan     0.1000   -0.0006
   220        0.7431             nan     0.1000   -0.0004
   240        0.7397             nan     0.1000   -0.0007
   260        0.7348             nan     0.1000   -0.0005
   280        0.7287             nan     0.1000   -0.0009
   300        0.7242             nan     0.1000   -0.0005
   320        0.7195             nan     0.1000   -0.0011
   340        0.7158             nan     0.1000   -0.0005
   360        0.7114             nan     0.1000   -0.0007
   380        0.7072             nan     0.1000   -0.0005
   400        0.7044             nan     0.1000   -0.0006
   420        0.7008             nan     0.1000   -0.0002
   440        0.6983             nan     0.1000   -0.0002
   460        0.6970             nan     0.1000   -0.0004
   480        0.6936             nan     0.1000   -0.0011
   500        0.6910             nan     0.1000   -0.0004
   520        0.6888             nan     0.1000   -0.0013
   540        0.6875             nan     0.1000   -0.0008
   560        0.6857             nan     0.1000   -0.0007
   580        0.6840             nan     0.1000   -0.0007
   600        0.6819             nan     0.1000   -0.0006
   620        0.6803             nan     0.1000   -0.0009
   640        0.6781             nan     0.1000   -0.0005
   660        0.6762             nan     0.1000   -0.0005
   680        0.6748             nan     0.1000   -0.0007
   700        0.6734             nan     0.1000   -0.0010
   720        0.6715             nan     0.1000   -0.0012
   740        0.6701             nan     0.1000   -0.0003
   760        0.6674             nan     0.1000   -0.0001
   780        0.6658             nan     0.1000   -0.0009
   800        0.6633             nan     0.1000   -0.0008
   820        0.6609             nan     0.1000   -0.0008
   840        0.6590             nan     0.1000   -0.0012
   860        0.6575             nan     0.1000   -0.0007
   880        0.6567             nan     0.1000   -0.0008
   900        0.6556             nan     0.1000   -0.0012
   920        0.6535             nan     0.1000   -0.0002
   940        0.6528             nan     0.1000   -0.0004
   960        0.6505             nan     0.1000   -0.0006
   980        0.6505             nan     0.1000   -0.0008
  1000        0.6478             nan     0.1000   -0.0009
  1020        0.6460             nan     0.1000   -0.0004
  1040        0.6435             nan     0.1000   -0.0009
  1060        0.6430             nan     0.1000   -0.0011
  1080        0.6418             nan     0.1000   -0.0006
  1100        0.6407             nan     0.1000   -0.0007

- Fold07.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2600             nan     0.1000    0.0317
     2        1.2054             nan     0.1000    0.0262
     3        1.1511             nan     0.1000    0.0230
     4        1.1099             nan     0.1000    0.0205
     5        1.0752             nan     0.1000    0.0163
     6        1.0420             nan     0.1000    0.0138
     7        1.0182             nan     0.1000    0.0119
     8        0.9941             nan     0.1000    0.0105
     9        0.9734             nan     0.1000    0.0090
    10        0.9583             nan     0.1000    0.0066
    20        0.8608             nan     0.1000    0.0035
    40        0.7920             nan     0.1000   -0.0004
    60        0.7567             nan     0.1000   -0.0004
    80        0.7305             nan     0.1000   -0.0000
   100        0.7096             nan     0.1000   -0.0015
   120        0.6896             nan     0.1000   -0.0002
   140        0.6732             nan     0.1000   -0.0013
   160        0.6587             nan     0.1000   -0.0007
   180        0.6448             nan     0.1000   -0.0004
   200        0.6304             nan     0.1000   -0.0016
   220        0.6196             nan     0.1000    0.0002
   240        0.6094             nan     0.1000   -0.0009
   260        0.6011             nan     0.1000   -0.0010
   280        0.5937             nan     0.1000   -0.0007
   300        0.5806             nan     0.1000   -0.0013
   320        0.5750             nan     0.1000   -0.0004
   340        0.5673             nan     0.1000   -0.0013
   360        0.5596             nan     0.1000   -0.0014
   380        0.5523             nan     0.1000   -0.0013
   400        0.5433             nan     0.1000   -0.0016
   420        0.5369             nan     0.1000   -0.0009
   440        0.5317             nan     0.1000   -0.0009
   460        0.5243             nan     0.1000   -0.0007
   480        0.5160             nan     0.1000   -0.0009
   500        0.5078             nan     0.1000   -0.0007
   520        0.5037             nan     0.1000   -0.0011
   540        0.4969             nan     0.1000   -0.0007
   560        0.4933             nan     0.1000   -0.0006
   580        0.4901             nan     0.1000   -0.0010
   600        0.4860             nan     0.1000   -0.0011
   620        0.4805             nan     0.1000   -0.0016
   640        0.4764             nan     0.1000   -0.0010
   660        0.4723             nan     0.1000   -0.0007
   680        0.4690             nan     0.1000   -0.0006
   700        0.4637             nan     0.1000   -0.0012
   720        0.4593             nan     0.1000   -0.0011
   740        0.4560             nan     0.1000   -0.0003
   760        0.4497             nan     0.1000   -0.0010
   780        0.4443             nan     0.1000   -0.0010
   800        0.4410             nan     0.1000   -0.0004
   820        0.4379             nan     0.1000   -0.0010
   840        0.4327             nan     0.1000   -0.0011
   860        0.4302             nan     0.1000   -0.0016
   880        0.4271             nan     0.1000   -0.0006
   900        0.4237             nan     0.1000   -0.0005
   920        0.4205             nan     0.1000   -0.0007
   940        0.4176             nan     0.1000   -0.0005
   960        0.4164             nan     0.1000   -0.0004
   980        0.4129             nan     0.1000   -0.0010
  1000        0.4083             nan     0.1000   -0.0006
  1020        0.4060             nan     0.1000   -0.0007
  1040        0.4029             nan     0.1000   -0.0005
  1060        0.3998             nan     0.1000   -0.0011
  1080        0.3960             nan     0.1000   -0.0004
  1100        0.3942             nan     0.1000   -0.0012

- Fold07.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2527             nan     0.1000    0.0402
     2        1.1868             nan     0.1000    0.0311
     3        1.1335             nan     0.1000    0.0262
     4        1.0895             nan     0.1000    0.0211
     5        1.0524             nan     0.1000    0.0190
     6        1.0201             nan     0.1000    0.0160
     7        0.9909             nan     0.1000    0.0115
     8        0.9636             nan     0.1000    0.0111
     9        0.9436             nan     0.1000    0.0105
    10        0.9258             nan     0.1000    0.0083
    20        0.8287             nan     0.1000    0.0023
    40        0.7498             nan     0.1000   -0.0016
    60        0.7049             nan     0.1000   -0.0019
    80        0.6696             nan     0.1000   -0.0014
   100        0.6433             nan     0.1000   -0.0008
   120        0.6252             nan     0.1000   -0.0009
   140        0.6051             nan     0.1000   -0.0014
   160        0.5895             nan     0.1000   -0.0011
   180        0.5711             nan     0.1000   -0.0019
   200        0.5539             nan     0.1000   -0.0013
   220        0.5419             nan     0.1000   -0.0008
   240        0.5260             nan     0.1000   -0.0013
   260        0.5147             nan     0.1000   -0.0006
   280        0.5027             nan     0.1000   -0.0028
   300        0.4895             nan     0.1000   -0.0015
   320        0.4802             nan     0.1000   -0.0009
   340        0.4705             nan     0.1000   -0.0007
   360        0.4615             nan     0.1000   -0.0004
   380        0.4504             nan     0.1000   -0.0007
   400        0.4420             nan     0.1000   -0.0017
   420        0.4306             nan     0.1000   -0.0018
   440        0.4214             nan     0.1000   -0.0003
   460        0.4145             nan     0.1000   -0.0023
   480        0.4078             nan     0.1000   -0.0008
   500        0.4007             nan     0.1000   -0.0008
   520        0.3930             nan     0.1000   -0.0011
   540        0.3855             nan     0.1000   -0.0008
   560        0.3803             nan     0.1000   -0.0005
   580        0.3726             nan     0.1000   -0.0012
   600        0.3658             nan     0.1000   -0.0017
   620        0.3594             nan     0.1000   -0.0007
   640        0.3547             nan     0.1000   -0.0010
   660        0.3474             nan     0.1000   -0.0007
   680        0.3435             nan     0.1000   -0.0010
   700        0.3382             nan     0.1000   -0.0004
   720        0.3332             nan     0.1000   -0.0009
   740        0.3273             nan     0.1000   -0.0007
   760        0.3242             nan     0.1000   -0.0008
   780        0.3180             nan     0.1000   -0.0007
   800        0.3142             nan     0.1000   -0.0002
   820        0.3104             nan     0.1000   -0.0005
   840        0.3076             nan     0.1000   -0.0006
   860        0.3045             nan     0.1000   -0.0002
   880        0.3006             nan     0.1000   -0.0007
   900        0.2965             nan     0.1000   -0.0019
   920        0.2927             nan     0.1000   -0.0004
   940        0.2885             nan     0.1000   -0.0009
   960        0.2832             nan     0.1000   -0.0003
   980        0.2801             nan     0.1000   -0.0004
  1000        0.2775             nan     0.1000   -0.0008
  1020        0.2739             nan     0.1000   -0.0010
  1040        0.2706             nan     0.1000   -0.0008
  1060        0.2672             nan     0.1000   -0.0003
  1080        0.2640             nan     0.1000   -0.0005
  1100        0.2606             nan     0.1000   -0.0010

- Fold07.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3261             nan     0.0100    0.0028
     2        1.3202             nan     0.0100    0.0029
     3        1.3143             nan     0.0100    0.0028
     4        1.3090             nan     0.0100    0.0026
     5        1.3033             nan     0.0100    0.0027
     6        1.2979             nan     0.0100    0.0026
     7        1.2922             nan     0.0100    0.0024
     8        1.2869             nan     0.0100    0.0026
     9        1.2817             nan     0.0100    0.0025
    10        1.2768             nan     0.0100    0.0023
    20        1.2337             nan     0.0100    0.0019
    40        1.1675             nan     0.0100    0.0013
    60        1.1231             nan     0.0100    0.0008
    80        1.0876             nan     0.0100    0.0007
   100        1.0580             nan     0.0100    0.0004
   120        1.0341             nan     0.0100    0.0005
   140        1.0132             nan     0.0100    0.0004
   160        0.9957             nan     0.0100    0.0003
   180        0.9812             nan     0.0100    0.0002
   200        0.9679             nan     0.0100    0.0001
   220        0.9555             nan     0.0100    0.0002
   240        0.9447             nan     0.0100    0.0002
   260        0.9353             nan     0.0100    0.0001
   280        0.9270             nan     0.0100    0.0002
   300        0.9189             nan     0.0100    0.0001
   320        0.9116             nan     0.0100    0.0001
   340        0.9050             nan     0.0100    0.0000
   360        0.8985             nan     0.0100    0.0001
   380        0.8925             nan     0.0100    0.0001
   400        0.8869             nan     0.0100    0.0001
   420        0.8818             nan     0.0100    0.0000
   440        0.8769             nan     0.0100    0.0001
   460        0.8721             nan     0.0100    0.0000
   480        0.8679             nan     0.0100   -0.0001
   500        0.8641             nan     0.0100    0.0000
   520        0.8600             nan     0.0100    0.0000
   540        0.8564             nan     0.0100    0.0000
   560        0.8528             nan     0.0100    0.0000
   580        0.8491             nan     0.0100    0.0000
   600        0.8457             nan     0.0100    0.0001
   620        0.8424             nan     0.0100   -0.0000
   640        0.8391             nan     0.0100    0.0000
   660        0.8361             nan     0.0100   -0.0000
   680        0.8334             nan     0.0100    0.0000
   700        0.8306             nan     0.0100    0.0000
   720        0.8279             nan     0.0100    0.0000
   740        0.8253             nan     0.0100   -0.0000
   760        0.8228             nan     0.0100    0.0001
   780        0.8202             nan     0.0100    0.0000
   800        0.8179             nan     0.0100   -0.0000
   820        0.8158             nan     0.0100   -0.0001
   840        0.8134             nan     0.0100    0.0000
   860        0.8113             nan     0.0100   -0.0001
   880        0.8091             nan     0.0100   -0.0000
   900        0.8070             nan     0.0100   -0.0000
   920        0.8052             nan     0.0100   -0.0000
   940        0.8033             nan     0.0100   -0.0001
   960        0.8015             nan     0.0100    0.0000
   980        0.8001             nan     0.0100   -0.0000
  1000        0.7981             nan     0.0100   -0.0001
  1020        0.7966             nan     0.0100   -0.0001
  1040        0.7950             nan     0.0100   -0.0000
  1060        0.7932             nan     0.0100   -0.0000
  1080        0.7917             nan     0.0100   -0.0001
  1100        0.7903             nan     0.0100    0.0000

- Fold08.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3245             nan     0.0100    0.0038
     2        1.3176             nan     0.0100    0.0036
     3        1.3112             nan     0.0100    0.0030
     4        1.3046             nan     0.0100    0.0033
     5        1.2979             nan     0.0100    0.0033
     6        1.2918             nan     0.0100    0.0034
     7        1.2853             nan     0.0100    0.0031
     8        1.2787             nan     0.0100    0.0032
     9        1.2727             nan     0.0100    0.0031
    10        1.2666             nan     0.0100    0.0028
    20        1.2105             nan     0.0100    0.0025
    40        1.1262             nan     0.0100    0.0017
    60        1.0631             nan     0.0100    0.0012
    80        1.0160             nan     0.0100    0.0009
   100        0.9792             nan     0.0100    0.0008
   120        0.9501             nan     0.0100    0.0004
   140        0.9273             nan     0.0100    0.0003
   160        0.9081             nan     0.0100    0.0001
   180        0.8922             nan     0.0100    0.0003
   200        0.8786             nan     0.0100    0.0002
   220        0.8668             nan     0.0100    0.0001
   240        0.8562             nan     0.0100    0.0002
   260        0.8469             nan     0.0100    0.0000
   280        0.8379             nan     0.0100    0.0001
   300        0.8296             nan     0.0100    0.0001
   320        0.8226             nan     0.0100    0.0000
   340        0.8152             nan     0.0100    0.0000
   360        0.8087             nan     0.0100   -0.0000
   380        0.8027             nan     0.0100   -0.0000
   400        0.7972             nan     0.0100   -0.0000
   420        0.7924             nan     0.0100   -0.0001
   440        0.7878             nan     0.0100   -0.0001
   460        0.7832             nan     0.0100   -0.0001
   480        0.7786             nan     0.0100   -0.0001
   500        0.7747             nan     0.0100   -0.0000
   520        0.7708             nan     0.0100   -0.0000
   540        0.7673             nan     0.0100   -0.0001
   560        0.7638             nan     0.0100    0.0001
   580        0.7603             nan     0.0100   -0.0001
   600        0.7570             nan     0.0100   -0.0000
   620        0.7541             nan     0.0100    0.0001
   640        0.7509             nan     0.0100   -0.0001
   660        0.7479             nan     0.0100   -0.0001
   680        0.7451             nan     0.0100   -0.0001
   700        0.7421             nan     0.0100   -0.0001
   720        0.7396             nan     0.0100   -0.0001
   740        0.7372             nan     0.0100   -0.0001
   760        0.7347             nan     0.0100   -0.0001
   780        0.7321             nan     0.0100   -0.0001
   800        0.7298             nan     0.0100    0.0000
   820        0.7275             nan     0.0100   -0.0000
   840        0.7255             nan     0.0100   -0.0001
   860        0.7229             nan     0.0100   -0.0001
   880        0.7207             nan     0.0100    0.0000
   900        0.7184             nan     0.0100   -0.0000
   920        0.7163             nan     0.0100   -0.0000
   940        0.7145             nan     0.0100   -0.0001
   960        0.7121             nan     0.0100   -0.0001
   980        0.7100             nan     0.0100   -0.0001
  1000        0.7081             nan     0.0100   -0.0002
  1020        0.7056             nan     0.0100   -0.0001
  1040        0.7037             nan     0.0100   -0.0000
  1060        0.7015             nan     0.0100   -0.0001
  1080        0.6995             nan     0.0100   -0.0001
  1100        0.6981             nan     0.0100   -0.0001

- Fold08.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0041
     2        1.3171             nan     0.0100    0.0036
     3        1.3095             nan     0.0100    0.0035
     4        1.3022             nan     0.0100    0.0038
     5        1.2949             nan     0.0100    0.0036
     6        1.2880             nan     0.0100    0.0036
     7        1.2807             nan     0.0100    0.0037
     8        1.2734             nan     0.0100    0.0034
     9        1.2666             nan     0.0100    0.0033
    10        1.2601             nan     0.0100    0.0032
    20        1.1999             nan     0.0100    0.0027
    40        1.1037             nan     0.0100    0.0019
    60        1.0347             nan     0.0100    0.0014
    80        0.9839             nan     0.0100    0.0010
   100        0.9450             nan     0.0100    0.0004
   120        0.9138             nan     0.0100    0.0006
   140        0.8892             nan     0.0100    0.0003
   160        0.8686             nan     0.0100    0.0002
   180        0.8516             nan     0.0100    0.0002
   200        0.8365             nan     0.0100    0.0003
   220        0.8241             nan     0.0100    0.0001
   240        0.8134             nan     0.0100    0.0000
   260        0.8030             nan     0.0100    0.0001
   280        0.7942             nan     0.0100    0.0000
   300        0.7863             nan     0.0100    0.0000
   320        0.7787             nan     0.0100    0.0001
   340        0.7721             nan     0.0100   -0.0000
   360        0.7652             nan     0.0100   -0.0001
   380        0.7585             nan     0.0100   -0.0001
   400        0.7530             nan     0.0100   -0.0000
   420        0.7481             nan     0.0100   -0.0001
   440        0.7432             nan     0.0100   -0.0000
   460        0.7384             nan     0.0100   -0.0000
   480        0.7338             nan     0.0100    0.0000
   500        0.7294             nan     0.0100   -0.0001
   520        0.7249             nan     0.0100   -0.0001
   540        0.7203             nan     0.0100   -0.0001
   560        0.7164             nan     0.0100   -0.0001
   580        0.7124             nan     0.0100   -0.0001
   600        0.7088             nan     0.0100   -0.0001
   620        0.7055             nan     0.0100   -0.0001
   640        0.7019             nan     0.0100   -0.0001
   660        0.6987             nan     0.0100   -0.0002
   680        0.6953             nan     0.0100   -0.0001
   700        0.6921             nan     0.0100   -0.0000
   720        0.6887             nan     0.0100   -0.0001
   740        0.6861             nan     0.0100   -0.0001
   760        0.6833             nan     0.0100   -0.0000
   780        0.6805             nan     0.0100   -0.0002
   800        0.6778             nan     0.0100   -0.0001
   820        0.6755             nan     0.0100    0.0000
   840        0.6729             nan     0.0100   -0.0002
   860        0.6696             nan     0.0100   -0.0001
   880        0.6666             nan     0.0100   -0.0001
   900        0.6639             nan     0.0100   -0.0000
   920        0.6612             nan     0.0100   -0.0001
   940        0.6584             nan     0.0100   -0.0001
   960        0.6558             nan     0.0100    0.0000
   980        0.6535             nan     0.0100   -0.0001
  1000        0.6507             nan     0.0100   -0.0000
  1020        0.6483             nan     0.0100   -0.0000
  1040        0.6456             nan     0.0100   -0.0001
  1060        0.6434             nan     0.0100   -0.0001
  1080        0.6412             nan     0.0100   -0.0001
  1100        0.6387             nan     0.0100   -0.0001

- Fold08.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2773             nan     0.1000    0.0273
     2        1.2345             nan     0.1000    0.0218
     3        1.1992             nan     0.1000    0.0186
     4        1.1681             nan     0.1000    0.0154
     5        1.1422             nan     0.1000    0.0125
     6        1.1218             nan     0.1000    0.0102
     7        1.1047             nan     0.1000    0.0089
     8        1.0877             nan     0.1000    0.0068
     9        1.0713             nan     0.1000    0.0058
    10        1.0574             nan     0.1000    0.0070
    20        0.9649             nan     0.1000    0.0024
    40        0.8868             nan     0.1000   -0.0001
    60        0.8442             nan     0.1000    0.0003
    80        0.8192             nan     0.1000   -0.0002
   100        0.8001             nan     0.1000   -0.0001
   120        0.7863             nan     0.1000    0.0002
   140        0.7748             nan     0.1000   -0.0003
   160        0.7643             nan     0.1000   -0.0007
   180        0.7574             nan     0.1000   -0.0012
   200        0.7509             nan     0.1000   -0.0009
   220        0.7437             nan     0.1000   -0.0003
   240        0.7377             nan     0.1000   -0.0005
   260        0.7322             nan     0.1000   -0.0004
   280        0.7265             nan     0.1000   -0.0011
   300        0.7222             nan     0.1000   -0.0001
   320        0.7192             nan     0.1000   -0.0005
   340        0.7140             nan     0.1000   -0.0011
   360        0.7100             nan     0.1000   -0.0003
   380        0.7073             nan     0.1000   -0.0001
   400        0.7040             nan     0.1000   -0.0008
   420        0.6996             nan     0.1000   -0.0005
   440        0.6966             nan     0.1000   -0.0011
   460        0.6933             nan     0.1000   -0.0007
   480        0.6908             nan     0.1000   -0.0005
   500        0.6888             nan     0.1000   -0.0010
   520        0.6836             nan     0.1000   -0.0008
   540        0.6823             nan     0.1000   -0.0005
   560        0.6810             nan     0.1000   -0.0006
   580        0.6799             nan     0.1000   -0.0009
   600        0.6775             nan     0.1000   -0.0008
   620        0.6760             nan     0.1000   -0.0005
   640        0.6747             nan     0.1000   -0.0005
   660        0.6716             nan     0.1000   -0.0009
   680        0.6701             nan     0.1000   -0.0004
   700        0.6673             nan     0.1000   -0.0006
   720        0.6655             nan     0.1000   -0.0009
   740        0.6635             nan     0.1000   -0.0005
   760        0.6621             nan     0.1000   -0.0000
   780        0.6591             nan     0.1000   -0.0005
   800        0.6578             nan     0.1000   -0.0003
   820        0.6567             nan     0.1000   -0.0008
   840        0.6557             nan     0.1000   -0.0015
   860        0.6531             nan     0.1000   -0.0005
   880        0.6528             nan     0.1000   -0.0019
   900        0.6518             nan     0.1000   -0.0007
   920        0.6502             nan     0.1000   -0.0005
   940        0.6493             nan     0.1000   -0.0011
   960        0.6477             nan     0.1000   -0.0007
   980        0.6470             nan     0.1000   -0.0012
  1000        0.6457             nan     0.1000   -0.0022
  1020        0.6453             nan     0.1000   -0.0009
  1040        0.6430             nan     0.1000   -0.0008
  1060        0.6429             nan     0.1000   -0.0015
  1080        0.6419             nan     0.1000   -0.0007
  1100        0.6396             nan     0.1000   -0.0003

- Fold08.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2620             nan     0.1000    0.0341
     2        1.2027             nan     0.1000    0.0269
     3        1.1514             nan     0.1000    0.0233
     4        1.1143             nan     0.1000    0.0188
     5        1.0821             nan     0.1000    0.0135
     6        1.0526             nan     0.1000    0.0131
     7        1.0313             nan     0.1000    0.0110
     8        1.0120             nan     0.1000    0.0086
     9        0.9944             nan     0.1000    0.0099
    10        0.9776             nan     0.1000    0.0071
    20        0.8805             nan     0.1000    0.0016
    40        0.7984             nan     0.1000   -0.0005
    60        0.7613             nan     0.1000   -0.0008
    80        0.7266             nan     0.1000   -0.0003
   100        0.7071             nan     0.1000   -0.0005
   120        0.6893             nan     0.1000   -0.0007
   140        0.6716             nan     0.1000   -0.0010
   160        0.6584             nan     0.1000   -0.0016
   180        0.6447             nan     0.1000   -0.0007
   200        0.6317             nan     0.1000   -0.0022
   220        0.6199             nan     0.1000   -0.0012
   240        0.6075             nan     0.1000   -0.0005
   260        0.5976             nan     0.1000   -0.0009
   280        0.5896             nan     0.1000   -0.0014
   300        0.5824             nan     0.1000   -0.0012
   320        0.5734             nan     0.1000   -0.0016
   340        0.5645             nan     0.1000   -0.0013
   360        0.5556             nan     0.1000   -0.0009
   380        0.5472             nan     0.1000   -0.0013
   400        0.5407             nan     0.1000   -0.0008
   420        0.5368             nan     0.1000   -0.0008
   440        0.5313             nan     0.1000   -0.0008
   460        0.5262             nan     0.1000   -0.0010
   480        0.5224             nan     0.1000   -0.0007
   500        0.5178             nan     0.1000   -0.0010
   520        0.5110             nan     0.1000   -0.0004
   540        0.5059             nan     0.1000   -0.0004
   560        0.5001             nan     0.1000   -0.0008
   580        0.4947             nan     0.1000   -0.0007
   600        0.4881             nan     0.1000   -0.0007
   620        0.4807             nan     0.1000   -0.0018
   640        0.4755             nan     0.1000   -0.0010
   660        0.4717             nan     0.1000   -0.0003
   680        0.4685             nan     0.1000   -0.0008
   700        0.4647             nan     0.1000   -0.0014
   720        0.4609             nan     0.1000   -0.0007
   740        0.4564             nan     0.1000   -0.0005
   760        0.4519             nan     0.1000   -0.0007
   780        0.4491             nan     0.1000   -0.0009
   800        0.4452             nan     0.1000   -0.0012
   820        0.4414             nan     0.1000   -0.0004
   840        0.4361             nan     0.1000   -0.0003
   860        0.4340             nan     0.1000   -0.0013
   880        0.4307             nan     0.1000   -0.0006
   900        0.4267             nan     0.1000   -0.0004
   920        0.4233             nan     0.1000   -0.0011
   940        0.4190             nan     0.1000   -0.0012
   960        0.4157             nan     0.1000   -0.0007
   980        0.4120             nan     0.1000   -0.0005
  1000        0.4090             nan     0.1000   -0.0008
  1020        0.4059             nan     0.1000   -0.0004
  1040        0.4019             nan     0.1000   -0.0014
  1060        0.4003             nan     0.1000   -0.0004
  1080        0.3973             nan     0.1000   -0.0008
  1100        0.3941             nan     0.1000   -0.0012

- Fold08.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2554             nan     0.1000    0.0386
     2        1.1970             nan     0.1000    0.0298
     3        1.1458             nan     0.1000    0.0266
     4        1.1021             nan     0.1000    0.0177
     5        1.0658             nan     0.1000    0.0141
     6        1.0341             nan     0.1000    0.0152
     7        1.0050             nan     0.1000    0.0136
     8        0.9807             nan     0.1000    0.0123
     9        0.9590             nan     0.1000    0.0092
    10        0.9436             nan     0.1000    0.0057
    20        0.8394             nan     0.1000    0.0013
    40        0.7604             nan     0.1000   -0.0013
    60        0.7141             nan     0.1000   -0.0008
    80        0.6813             nan     0.1000   -0.0017
   100        0.6484             nan     0.1000   -0.0009
   120        0.6276             nan     0.1000   -0.0017
   140        0.6060             nan     0.1000   -0.0012
   160        0.5884             nan     0.1000   -0.0020
   180        0.5698             nan     0.1000   -0.0018
   200        0.5552             nan     0.1000   -0.0023
   220        0.5397             nan     0.1000   -0.0019
   240        0.5283             nan     0.1000   -0.0013
   260        0.5146             nan     0.1000   -0.0005
   280        0.5023             nan     0.1000   -0.0006
   300        0.4939             nan     0.1000   -0.0019
   320        0.4867             nan     0.1000   -0.0012
   340        0.4773             nan     0.1000   -0.0011
   360        0.4695             nan     0.1000   -0.0022
   380        0.4589             nan     0.1000   -0.0012
   400        0.4523             nan     0.1000   -0.0011
   420        0.4436             nan     0.1000   -0.0012
   440        0.4336             nan     0.1000   -0.0006
   460        0.4274             nan     0.1000   -0.0012
   480        0.4184             nan     0.1000   -0.0005
   500        0.4120             nan     0.1000   -0.0008
   520        0.4049             nan     0.1000   -0.0007
   540        0.4000             nan     0.1000   -0.0009
   560        0.3938             nan     0.1000   -0.0005
   580        0.3879             nan     0.1000   -0.0011
   600        0.3819             nan     0.1000   -0.0011
   620        0.3727             nan     0.1000   -0.0012
   640        0.3683             nan     0.1000   -0.0012
   660        0.3634             nan     0.1000   -0.0009
   680        0.3578             nan     0.1000   -0.0004
   700        0.3529             nan     0.1000   -0.0012
   720        0.3464             nan     0.1000   -0.0008
   740        0.3389             nan     0.1000   -0.0008
   760        0.3335             nan     0.1000   -0.0010
   780        0.3284             nan     0.1000   -0.0009
   800        0.3259             nan     0.1000   -0.0012
   820        0.3206             nan     0.1000   -0.0005
   840        0.3150             nan     0.1000   -0.0010
   860        0.3106             nan     0.1000   -0.0013
   880        0.3069             nan     0.1000   -0.0007
   900        0.3030             nan     0.1000   -0.0003
   920        0.2989             nan     0.1000   -0.0005
   940        0.2975             nan     0.1000   -0.0009
   960        0.2930             nan     0.1000   -0.0008
   980        0.2880             nan     0.1000   -0.0012
  1000        0.2839             nan     0.1000   -0.0007
  1020        0.2802             nan     0.1000   -0.0014
  1040        0.2770             nan     0.1000   -0.0007
  1060        0.2724             nan     0.1000   -0.0004
  1080        0.2706             nan     0.1000   -0.0016
  1100        0.2655             nan     0.1000   -0.0007

- Fold08.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3259             nan     0.0100    0.0031
     2        1.3198             nan     0.0100    0.0029
     3        1.3146             nan     0.0100    0.0029
     4        1.3085             nan     0.0100    0.0029
     5        1.3028             nan     0.0100    0.0027
     6        1.2978             nan     0.0100    0.0028
     7        1.2922             nan     0.0100    0.0026
     8        1.2868             nan     0.0100    0.0027
     9        1.2817             nan     0.0100    0.0025
    10        1.2759             nan     0.0100    0.0026
    20        1.2284             nan     0.0100    0.0020
    40        1.1555             nan     0.0100    0.0014
    60        1.1058             nan     0.0100    0.0010
    80        1.0680             nan     0.0100    0.0008
   100        1.0371             nan     0.0100    0.0006
   120        1.0114             nan     0.0100    0.0005
   140        0.9893             nan     0.0100    0.0004
   160        0.9708             nan     0.0100    0.0003
   180        0.9556             nan     0.0100    0.0003
   200        0.9414             nan     0.0100    0.0003
   220        0.9293             nan     0.0100    0.0001
   240        0.9185             nan     0.0100    0.0002
   260        0.9088             nan     0.0100    0.0001
   280        0.8994             nan     0.0100    0.0001
   300        0.8915             nan     0.0100    0.0001
   320        0.8839             nan     0.0100    0.0001
   340        0.8779             nan     0.0100    0.0001
   360        0.8717             nan     0.0100    0.0001
   380        0.8654             nan     0.0100    0.0001
   400        0.8602             nan     0.0100    0.0001
   420        0.8554             nan     0.0100    0.0001
   440        0.8503             nan     0.0100    0.0001
   460        0.8461             nan     0.0100    0.0001
   480        0.8420             nan     0.0100   -0.0000
   500        0.8385             nan     0.0100    0.0000
   520        0.8349             nan     0.0100   -0.0000
   540        0.8316             nan     0.0100    0.0001
   560        0.8281             nan     0.0100    0.0000
   580        0.8250             nan     0.0100   -0.0001
   600        0.8221             nan     0.0100   -0.0000
   620        0.8189             nan     0.0100   -0.0000
   640        0.8164             nan     0.0100    0.0000
   660        0.8137             nan     0.0100    0.0000
   680        0.8111             nan     0.0100   -0.0000
   700        0.8088             nan     0.0100    0.0000
   720        0.8063             nan     0.0100   -0.0000
   740        0.8039             nan     0.0100   -0.0001
   760        0.8017             nan     0.0100   -0.0000
   780        0.7993             nan     0.0100    0.0000
   800        0.7972             nan     0.0100    0.0000
   820        0.7950             nan     0.0100   -0.0000
   840        0.7927             nan     0.0100    0.0000
   860        0.7909             nan     0.0100   -0.0001
   880        0.7890             nan     0.0100   -0.0001
   900        0.7873             nan     0.0100   -0.0000
   920        0.7854             nan     0.0100   -0.0001
   940        0.7837             nan     0.0100    0.0000
   960        0.7823             nan     0.0100   -0.0001
   980        0.7807             nan     0.0100   -0.0000
  1000        0.7789             nan     0.0100   -0.0000
  1020        0.7772             nan     0.0100   -0.0001
  1040        0.7755             nan     0.0100   -0.0001
  1060        0.7741             nan     0.0100   -0.0001
  1080        0.7727             nan     0.0100    0.0000
  1100        0.7716             nan     0.0100   -0.0000

- Fold09.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3247             nan     0.0100    0.0037
     2        1.3170             nan     0.0100    0.0037
     3        1.3093             nan     0.0100    0.0037
     4        1.3022             nan     0.0100    0.0033
     5        1.2952             nan     0.0100    0.0035
     6        1.2884             nan     0.0100    0.0032
     7        1.2822             nan     0.0100    0.0031
     8        1.2762             nan     0.0100    0.0032
     9        1.2692             nan     0.0100    0.0032
    10        1.2630             nan     0.0100    0.0033
    20        1.2041             nan     0.0100    0.0027
    40        1.1134             nan     0.0100    0.0018
    60        1.0479             nan     0.0100    0.0013
    80        0.9978             nan     0.0100    0.0010
   100        0.9594             nan     0.0100    0.0007
   120        0.9289             nan     0.0100    0.0006
   140        0.9050             nan     0.0100    0.0005
   160        0.8863             nan     0.0100    0.0001
   180        0.8702             nan     0.0100    0.0001
   200        0.8561             nan     0.0100    0.0001
   220        0.8443             nan     0.0100    0.0001
   240        0.8334             nan     0.0100    0.0001
   260        0.8248             nan     0.0100    0.0002
   280        0.8170             nan     0.0100   -0.0001
   300        0.8094             nan     0.0100    0.0000
   320        0.8023             nan     0.0100    0.0000
   340        0.7957             nan     0.0100    0.0001
   360        0.7900             nan     0.0100   -0.0001
   380        0.7844             nan     0.0100   -0.0000
   400        0.7792             nan     0.0100    0.0001
   420        0.7741             nan     0.0100   -0.0000
   440        0.7695             nan     0.0100    0.0001
   460        0.7652             nan     0.0100   -0.0001
   480        0.7612             nan     0.0100   -0.0001
   500        0.7574             nan     0.0100   -0.0001
   520        0.7537             nan     0.0100    0.0000
   540        0.7503             nan     0.0100    0.0000
   560        0.7474             nan     0.0100   -0.0001
   580        0.7441             nan     0.0100    0.0001
   600        0.7407             nan     0.0100   -0.0000
   620        0.7377             nan     0.0100   -0.0001
   640        0.7349             nan     0.0100   -0.0002
   660        0.7318             nan     0.0100   -0.0002
   680        0.7291             nan     0.0100   -0.0001
   700        0.7269             nan     0.0100   -0.0000
   720        0.7244             nan     0.0100   -0.0001
   740        0.7219             nan     0.0100   -0.0000
   760        0.7195             nan     0.0100    0.0000
   780        0.7174             nan     0.0100   -0.0002
   800        0.7151             nan     0.0100   -0.0001
   820        0.7127             nan     0.0100   -0.0001
   840        0.7104             nan     0.0100   -0.0001
   860        0.7079             nan     0.0100   -0.0000
   880        0.7059             nan     0.0100   -0.0001
   900        0.7034             nan     0.0100   -0.0001
   920        0.7013             nan     0.0100   -0.0000
   940        0.6994             nan     0.0100   -0.0001
   960        0.6975             nan     0.0100   -0.0001
   980        0.6954             nan     0.0100   -0.0001
  1000        0.6932             nan     0.0100   -0.0001
  1020        0.6913             nan     0.0100   -0.0001
  1040        0.6894             nan     0.0100   -0.0001
  1060        0.6873             nan     0.0100   -0.0000
  1080        0.6852             nan     0.0100   -0.0002
  1100        0.6836             nan     0.0100   -0.0002

- Fold09.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3239             nan     0.0100    0.0039
     2        1.3153             nan     0.0100    0.0040
     3        1.3069             nan     0.0100    0.0039
     4        1.2992             nan     0.0100    0.0037
     5        1.2921             nan     0.0100    0.0038
     6        1.2840             nan     0.0100    0.0035
     7        1.2765             nan     0.0100    0.0037
     8        1.2693             nan     0.0100    0.0034
     9        1.2619             nan     0.0100    0.0036
    10        1.2550             nan     0.0100    0.0036
    20        1.1936             nan     0.0100    0.0027
    40        1.0956             nan     0.0100    0.0017
    60        1.0229             nan     0.0100    0.0014
    80        0.9689             nan     0.0100    0.0011
   100        0.9278             nan     0.0100    0.0008
   120        0.8951             nan     0.0100    0.0005
   140        0.8696             nan     0.0100    0.0004
   160        0.8497             nan     0.0100    0.0003
   180        0.8338             nan     0.0100    0.0001
   200        0.8189             nan     0.0100    0.0001
   220        0.8067             nan     0.0100    0.0001
   240        0.7961             nan     0.0100   -0.0000
   260        0.7865             nan     0.0100    0.0001
   280        0.7772             nan     0.0100    0.0002
   300        0.7698             nan     0.0100   -0.0001
   320        0.7629             nan     0.0100    0.0001
   340        0.7566             nan     0.0100   -0.0001
   360        0.7501             nan     0.0100    0.0001
   380        0.7447             nan     0.0100   -0.0001
   400        0.7392             nan     0.0100    0.0001
   420        0.7335             nan     0.0100   -0.0001
   440        0.7284             nan     0.0100   -0.0001
   460        0.7236             nan     0.0100    0.0000
   480        0.7189             nan     0.0100   -0.0001
   500        0.7143             nan     0.0100   -0.0001
   520        0.7101             nan     0.0100   -0.0001
   540        0.7066             nan     0.0100   -0.0001
   560        0.7022             nan     0.0100   -0.0001
   580        0.6984             nan     0.0100   -0.0001
   600        0.6943             nan     0.0100   -0.0000
   620        0.6906             nan     0.0100   -0.0001
   640        0.6870             nan     0.0100   -0.0001
   660        0.6839             nan     0.0100   -0.0000
   680        0.6808             nan     0.0100   -0.0001
   700        0.6777             nan     0.0100   -0.0001
   720        0.6746             nan     0.0100   -0.0002
   740        0.6714             nan     0.0100   -0.0001
   760        0.6681             nan     0.0100    0.0000
   780        0.6648             nan     0.0100   -0.0001
   800        0.6621             nan     0.0100   -0.0001
   820        0.6590             nan     0.0100   -0.0003
   840        0.6563             nan     0.0100   -0.0001
   860        0.6535             nan     0.0100   -0.0000
   880        0.6504             nan     0.0100   -0.0001
   900        0.6482             nan     0.0100   -0.0001
   920        0.6452             nan     0.0100   -0.0002
   940        0.6429             nan     0.0100   -0.0001
   960        0.6405             nan     0.0100   -0.0001
   980        0.6380             nan     0.0100   -0.0001
  1000        0.6359             nan     0.0100   -0.0000
  1020        0.6333             nan     0.0100   -0.0002
  1040        0.6308             nan     0.0100   -0.0001
  1060        0.6287             nan     0.0100   -0.0001
  1080        0.6267             nan     0.0100   -0.0002
  1100        0.6246             nan     0.0100   -0.0001

- Fold09.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2780             nan     0.1000    0.0293
     2        1.2315             nan     0.1000    0.0240
     3        1.1873             nan     0.1000    0.0190
     4        1.1564             nan     0.1000    0.0171
     5        1.1273             nan     0.1000    0.0134
     6        1.1022             nan     0.1000    0.0112
     7        1.0813             nan     0.1000    0.0087
     8        1.0659             nan     0.1000    0.0077
     9        1.0483             nan     0.1000    0.0085
    10        1.0341             nan     0.1000    0.0066
    20        0.9393             nan     0.1000    0.0019
    40        0.8614             nan     0.1000    0.0002
    60        0.8261             nan     0.1000   -0.0005
    80        0.7963             nan     0.1000   -0.0000
   100        0.7793             nan     0.1000   -0.0000
   120        0.7648             nan     0.1000   -0.0004
   140        0.7513             nan     0.1000   -0.0002
   160        0.7435             nan     0.1000   -0.0008
   180        0.7359             nan     0.1000   -0.0008
   200        0.7318             nan     0.1000   -0.0005
   220        0.7248             nan     0.1000   -0.0011
   240        0.7180             nan     0.1000   -0.0003
   260        0.7134             nan     0.1000   -0.0003
   280        0.7084             nan     0.1000   -0.0004
   300        0.7045             nan     0.1000   -0.0004
   320        0.7013             nan     0.1000   -0.0007
   340        0.6983             nan     0.1000   -0.0009
   360        0.6945             nan     0.1000   -0.0009
   380        0.6931             nan     0.1000   -0.0016
   400        0.6905             nan     0.1000   -0.0008
   420        0.6879             nan     0.1000   -0.0012
   440        0.6850             nan     0.1000   -0.0006
   460        0.6816             nan     0.1000   -0.0006
   480        0.6792             nan     0.1000   -0.0004
   500        0.6778             nan     0.1000   -0.0008
   520        0.6745             nan     0.1000   -0.0004
   540        0.6726             nan     0.1000   -0.0008
   560        0.6707             nan     0.1000   -0.0018
   580        0.6693             nan     0.1000   -0.0006
   600        0.6684             nan     0.1000   -0.0013
   620        0.6657             nan     0.1000   -0.0013
   640        0.6644             nan     0.1000   -0.0008
   660        0.6618             nan     0.1000   -0.0006
   680        0.6603             nan     0.1000   -0.0008
   700        0.6594             nan     0.1000   -0.0004
   720        0.6577             nan     0.1000   -0.0006
   740        0.6555             nan     0.1000   -0.0002
   760        0.6534             nan     0.1000   -0.0013
   780        0.6520             nan     0.1000   -0.0011
   800        0.6510             nan     0.1000   -0.0004
   820        0.6503             nan     0.1000   -0.0010
   840        0.6483             nan     0.1000   -0.0005
   860        0.6478             nan     0.1000   -0.0009
   880        0.6472             nan     0.1000   -0.0013
   900        0.6448             nan     0.1000   -0.0007
   920        0.6437             nan     0.1000   -0.0007
   940        0.6417             nan     0.1000   -0.0008
   960        0.6402             nan     0.1000   -0.0004
   980        0.6377             nan     0.1000   -0.0013
  1000        0.6361             nan     0.1000   -0.0008
  1020        0.6349             nan     0.1000   -0.0005
  1040        0.6333             nan     0.1000   -0.0004
  1060        0.6326             nan     0.1000   -0.0003
  1080        0.6318             nan     0.1000   -0.0012
  1100        0.6308             nan     0.1000   -0.0008

- Fold09.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2591             nan     0.1000    0.0336
     2        1.1987             nan     0.1000    0.0312
     3        1.1488             nan     0.1000    0.0243
     4        1.1070             nan     0.1000    0.0195
     5        1.0700             nan     0.1000    0.0161
     6        1.0379             nan     0.1000    0.0151
     7        1.0082             nan     0.1000    0.0121
     8        0.9867             nan     0.1000    0.0106
     9        0.9676             nan     0.1000    0.0083
    10        0.9505             nan     0.1000    0.0068
    20        0.8488             nan     0.1000    0.0015
    40        0.7764             nan     0.1000   -0.0011
    60        0.7408             nan     0.1000   -0.0007
    80        0.7147             nan     0.1000   -0.0008
   100        0.6931             nan     0.1000   -0.0006
   120        0.6703             nan     0.1000   -0.0005
   140        0.6541             nan     0.1000   -0.0025
   160        0.6440             nan     0.1000   -0.0017
   180        0.6318             nan     0.1000   -0.0010
   200        0.6231             nan     0.1000   -0.0008
   220        0.6132             nan     0.1000   -0.0012
   240        0.6017             nan     0.1000   -0.0011
   260        0.5908             nan     0.1000   -0.0011
   280        0.5787             nan     0.1000   -0.0000
   300        0.5717             nan     0.1000   -0.0013
   320        0.5630             nan     0.1000   -0.0008
   340        0.5557             nan     0.1000   -0.0018
   360        0.5473             nan     0.1000   -0.0005
   380        0.5397             nan     0.1000   -0.0012
   400        0.5304             nan     0.1000   -0.0006
   420        0.5219             nan     0.1000    0.0000
   440        0.5150             nan     0.1000   -0.0022
   460        0.5098             nan     0.1000   -0.0008
   480        0.5041             nan     0.1000   -0.0005
   500        0.4988             nan     0.1000   -0.0006
   520        0.4921             nan     0.1000   -0.0012
   540        0.4861             nan     0.1000   -0.0005
   560        0.4814             nan     0.1000   -0.0012
   580        0.4774             nan     0.1000   -0.0005
   600        0.4726             nan     0.1000   -0.0007
   620        0.4689             nan     0.1000   -0.0015
   640        0.4660             nan     0.1000   -0.0007
   660        0.4613             nan     0.1000   -0.0007
   680        0.4573             nan     0.1000   -0.0007
   700        0.4537             nan     0.1000   -0.0010
   720        0.4490             nan     0.1000   -0.0003
   740        0.4458             nan     0.1000   -0.0011
   760        0.4411             nan     0.1000   -0.0010
   780        0.4381             nan     0.1000   -0.0008
   800        0.4326             nan     0.1000   -0.0006
   820        0.4285             nan     0.1000   -0.0009
   840        0.4239             nan     0.1000   -0.0009
   860        0.4207             nan     0.1000   -0.0008
   880        0.4170             nan     0.1000   -0.0019
   900        0.4123             nan     0.1000   -0.0005
   920        0.4089             nan     0.1000   -0.0012
   940        0.4051             nan     0.1000   -0.0006
   960        0.4012             nan     0.1000   -0.0010
   980        0.3994             nan     0.1000   -0.0010
  1000        0.3971             nan     0.1000   -0.0003
  1020        0.3928             nan     0.1000   -0.0007
  1040        0.3891             nan     0.1000   -0.0008
  1060        0.3857             nan     0.1000   -0.0008
  1080        0.3815             nan     0.1000   -0.0008
  1100        0.3791             nan     0.1000   -0.0011

- Fold09.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2583             nan     0.1000    0.0384
     2        1.1913             nan     0.1000    0.0321
     3        1.1359             nan     0.1000    0.0271
     4        1.0922             nan     0.1000    0.0224
     5        1.0529             nan     0.1000    0.0169
     6        1.0177             nan     0.1000    0.0159
     7        0.9893             nan     0.1000    0.0117
     8        0.9629             nan     0.1000    0.0121
     9        0.9384             nan     0.1000    0.0100
    10        0.9176             nan     0.1000    0.0081
    20        0.8185             nan     0.1000    0.0014
    40        0.7369             nan     0.1000   -0.0003
    60        0.7072             nan     0.1000   -0.0006
    80        0.6741             nan     0.1000   -0.0004
   100        0.6434             nan     0.1000   -0.0006
   120        0.6211             nan     0.1000   -0.0011
   140        0.6053             nan     0.1000   -0.0017
   160        0.5874             nan     0.1000   -0.0007
   180        0.5728             nan     0.1000   -0.0012
   200        0.5530             nan     0.1000   -0.0010
   220        0.5382             nan     0.1000   -0.0016
   240        0.5274             nan     0.1000   -0.0008
   260        0.5130             nan     0.1000   -0.0010
   280        0.5021             nan     0.1000   -0.0011
   300        0.4898             nan     0.1000   -0.0009
   320        0.4766             nan     0.1000   -0.0012
   340        0.4695             nan     0.1000   -0.0013
   360        0.4603             nan     0.1000   -0.0007
   380        0.4494             nan     0.1000   -0.0016
   400        0.4410             nan     0.1000   -0.0006
   420        0.4301             nan     0.1000   -0.0011
   440        0.4236             nan     0.1000   -0.0015
   460        0.4151             nan     0.1000   -0.0017
   480        0.4054             nan     0.1000   -0.0019
   500        0.3997             nan     0.1000   -0.0011
   520        0.3915             nan     0.1000   -0.0015
   540        0.3854             nan     0.1000   -0.0010
   560        0.3791             nan     0.1000   -0.0012
   580        0.3736             nan     0.1000   -0.0004
   600        0.3680             nan     0.1000   -0.0009
   620        0.3623             nan     0.1000   -0.0008
   640        0.3559             nan     0.1000   -0.0012
   660        0.3507             nan     0.1000   -0.0010
   680        0.3456             nan     0.1000   -0.0007
   700        0.3401             nan     0.1000   -0.0015
   720        0.3347             nan     0.1000   -0.0008
   740        0.3286             nan     0.1000   -0.0004
   760        0.3234             nan     0.1000   -0.0008
   780        0.3187             nan     0.1000   -0.0007
   800        0.3137             nan     0.1000   -0.0020
   820        0.3084             nan     0.1000   -0.0007
   840        0.3040             nan     0.1000   -0.0015
   860        0.2994             nan     0.1000   -0.0009
   880        0.2956             nan     0.1000   -0.0005
   900        0.2911             nan     0.1000   -0.0018
   920        0.2878             nan     0.1000   -0.0008
   940        0.2841             nan     0.1000   -0.0009
   960        0.2815             nan     0.1000   -0.0006
   980        0.2767             nan     0.1000   -0.0006
  1000        0.2731             nan     0.1000   -0.0009
  1020        0.2696             nan     0.1000   -0.0013
  1040        0.2669             nan     0.1000   -0.0008
  1060        0.2636             nan     0.1000   -0.0010
  1080        0.2604             nan     0.1000   -0.0012
  1100        0.2576             nan     0.1000   -0.0007

- Fold09.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3270             nan     0.0100    0.0028
     2        1.3214             nan     0.0100    0.0029
     3        1.3155             nan     0.0100    0.0026
     4        1.3102             nan     0.0100    0.0028
     5        1.3048             nan     0.0100    0.0027
     6        1.2993             nan     0.0100    0.0026
     7        1.2938             nan     0.0100    0.0025
     8        1.2892             nan     0.0100    0.0025
     9        1.2840             nan     0.0100    0.0025
    10        1.2796             nan     0.0100    0.0022
    20        1.2353             nan     0.0100    0.0021
    40        1.1682             nan     0.0100    0.0013
    60        1.1202             nan     0.0100    0.0010
    80        1.0848             nan     0.0100    0.0007
   100        1.0561             nan     0.0100    0.0005
   120        1.0310             nan     0.0100    0.0005
   140        1.0107             nan     0.0100    0.0003
   160        0.9927             nan     0.0100    0.0003
   180        0.9776             nan     0.0100    0.0003
   200        0.9654             nan     0.0100    0.0003
   220        0.9536             nan     0.0100    0.0002
   240        0.9435             nan     0.0100    0.0002
   260        0.9341             nan     0.0100    0.0001
   280        0.9262             nan     0.0100    0.0000
   300        0.9184             nan     0.0100    0.0002
   320        0.9118             nan     0.0100    0.0000
   340        0.9051             nan     0.0100    0.0001
   360        0.8994             nan     0.0100    0.0001
   380        0.8936             nan     0.0100    0.0001
   400        0.8884             nan     0.0100    0.0000
   420        0.8838             nan     0.0100   -0.0001
   440        0.8794             nan     0.0100   -0.0000
   460        0.8752             nan     0.0100    0.0000
   480        0.8713             nan     0.0100    0.0000
   500        0.8676             nan     0.0100   -0.0001
   520        0.8637             nan     0.0100   -0.0000
   540        0.8602             nan     0.0100   -0.0000
   560        0.8571             nan     0.0100    0.0000
   580        0.8536             nan     0.0100   -0.0000
   600        0.8506             nan     0.0100   -0.0000
   620        0.8479             nan     0.0100   -0.0000
   640        0.8451             nan     0.0100    0.0000
   660        0.8425             nan     0.0100    0.0000
   680        0.8400             nan     0.0100   -0.0000
   700        0.8375             nan     0.0100   -0.0001
   720        0.8354             nan     0.0100   -0.0000
   740        0.8331             nan     0.0100   -0.0000
   760        0.8309             nan     0.0100   -0.0001
   780        0.8288             nan     0.0100   -0.0001
   800        0.8267             nan     0.0100   -0.0000
   820        0.8246             nan     0.0100   -0.0000
   840        0.8225             nan     0.0100    0.0000
   860        0.8208             nan     0.0100   -0.0000
   880        0.8189             nan     0.0100   -0.0001
   900        0.8172             nan     0.0100    0.0000
   920        0.8155             nan     0.0100   -0.0000
   940        0.8138             nan     0.0100   -0.0000
   960        0.8122             nan     0.0100   -0.0000
   980        0.8108             nan     0.0100   -0.0000
  1000        0.8092             nan     0.0100   -0.0001
  1020        0.8077             nan     0.0100   -0.0001
  1040        0.8062             nan     0.0100   -0.0001
  1060        0.8047             nan     0.0100   -0.0001
  1080        0.8035             nan     0.0100   -0.0001
  1100        0.8023             nan     0.0100   -0.0000

- Fold10.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3253             nan     0.0100    0.0034
     2        1.3184             nan     0.0100    0.0035
     3        1.3113             nan     0.0100    0.0034
     4        1.3050             nan     0.0100    0.0034
     5        1.2984             nan     0.0100    0.0034
     6        1.2917             nan     0.0100    0.0033
     7        1.2853             nan     0.0100    0.0029
     8        1.2788             nan     0.0100    0.0031
     9        1.2725             nan     0.0100    0.0030
    10        1.2663             nan     0.0100    0.0027
    20        1.2106             nan     0.0100    0.0024
    40        1.1253             nan     0.0100    0.0017
    60        1.0628             nan     0.0100    0.0013
    80        1.0163             nan     0.0100    0.0009
   100        0.9798             nan     0.0100    0.0007
   120        0.9507             nan     0.0100    0.0005
   140        0.9275             nan     0.0100    0.0004
   160        0.9094             nan     0.0100    0.0002
   180        0.8943             nan     0.0100    0.0004
   200        0.8817             nan     0.0100    0.0001
   220        0.8703             nan     0.0100    0.0002
   240        0.8605             nan     0.0100   -0.0000
   260        0.8523             nan     0.0100    0.0001
   280        0.8444             nan     0.0100    0.0001
   300        0.8366             nan     0.0100    0.0001
   320        0.8295             nan     0.0100    0.0001
   340        0.8229             nan     0.0100    0.0001
   360        0.8166             nan     0.0100    0.0001
   380        0.8113             nan     0.0100    0.0001
   400        0.8057             nan     0.0100   -0.0000
   420        0.8010             nan     0.0100   -0.0000
   440        0.7964             nan     0.0100    0.0000
   460        0.7922             nan     0.0100   -0.0000
   480        0.7883             nan     0.0100   -0.0001
   500        0.7843             nan     0.0100   -0.0001
   520        0.7805             nan     0.0100   -0.0000
   540        0.7766             nan     0.0100   -0.0000
   560        0.7734             nan     0.0100   -0.0000
   580        0.7707             nan     0.0100    0.0000
   600        0.7674             nan     0.0100   -0.0001
   620        0.7644             nan     0.0100   -0.0001
   640        0.7613             nan     0.0100   -0.0000
   660        0.7582             nan     0.0100   -0.0001
   680        0.7556             nan     0.0100   -0.0002
   700        0.7530             nan     0.0100   -0.0002
   720        0.7506             nan     0.0100   -0.0001
   740        0.7481             nan     0.0100   -0.0001
   760        0.7462             nan     0.0100   -0.0001
   780        0.7434             nan     0.0100   -0.0000
   800        0.7412             nan     0.0100   -0.0002
   820        0.7390             nan     0.0100   -0.0000
   840        0.7368             nan     0.0100   -0.0001
   860        0.7347             nan     0.0100   -0.0001
   880        0.7324             nan     0.0100   -0.0001
   900        0.7303             nan     0.0100   -0.0001
   920        0.7277             nan     0.0100   -0.0001
   940        0.7252             nan     0.0100   -0.0001
   960        0.7230             nan     0.0100   -0.0000
   980        0.7209             nan     0.0100   -0.0000
  1000        0.7192             nan     0.0100    0.0000
  1020        0.7171             nan     0.0100   -0.0001
  1040        0.7155             nan     0.0100   -0.0001
  1060        0.7136             nan     0.0100   -0.0000
  1080        0.7115             nan     0.0100   -0.0000
  1100        0.7099             nan     0.0100   -0.0001

- Fold10.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0039
     2        1.3155             nan     0.0100    0.0038
     3        1.3080             nan     0.0100    0.0036
     4        1.3004             nan     0.0100    0.0035
     5        1.2932             nan     0.0100    0.0035
     6        1.2857             nan     0.0100    0.0035
     7        1.2787             nan     0.0100    0.0034
     8        1.2715             nan     0.0100    0.0034
     9        1.2642             nan     0.0100    0.0034
    10        1.2573             nan     0.0100    0.0032
    20        1.1971             nan     0.0100    0.0026
    40        1.1035             nan     0.0100    0.0017
    60        1.0342             nan     0.0100    0.0014
    80        0.9835             nan     0.0100    0.0011
   100        0.9446             nan     0.0100    0.0008
   120        0.9149             nan     0.0100    0.0005
   140        0.8919             nan     0.0100    0.0003
   160        0.8723             nan     0.0100    0.0003
   180        0.8564             nan     0.0100    0.0003
   200        0.8419             nan     0.0100    0.0002
   220        0.8301             nan     0.0100    0.0002
   240        0.8188             nan     0.0100    0.0001
   260        0.8088             nan     0.0100    0.0000
   280        0.7996             nan     0.0100    0.0002
   300        0.7910             nan     0.0100    0.0000
   320        0.7841             nan     0.0100   -0.0001
   340        0.7773             nan     0.0100   -0.0000
   360        0.7717             nan     0.0100   -0.0000
   380        0.7661             nan     0.0100   -0.0000
   400        0.7605             nan     0.0100   -0.0001
   420        0.7558             nan     0.0100    0.0000
   440        0.7505             nan     0.0100   -0.0000
   460        0.7452             nan     0.0100   -0.0001
   480        0.7412             nan     0.0100   -0.0002
   500        0.7368             nan     0.0100   -0.0000
   520        0.7328             nan     0.0100   -0.0001
   540        0.7281             nan     0.0100   -0.0001
   560        0.7231             nan     0.0100   -0.0001
   580        0.7191             nan     0.0100    0.0000
   600        0.7155             nan     0.0100   -0.0001
   620        0.7119             nan     0.0100   -0.0002
   640        0.7086             nan     0.0100   -0.0000
   660        0.7054             nan     0.0100   -0.0001
   680        0.7020             nan     0.0100   -0.0001
   700        0.6984             nan     0.0100   -0.0001
   720        0.6955             nan     0.0100   -0.0001
   740        0.6923             nan     0.0100   -0.0000
   760        0.6893             nan     0.0100   -0.0001
   780        0.6859             nan     0.0100   -0.0001
   800        0.6830             nan     0.0100   -0.0002
   820        0.6801             nan     0.0100   -0.0001
   840        0.6776             nan     0.0100   -0.0001
   860        0.6750             nan     0.0100   -0.0002
   880        0.6721             nan     0.0100   -0.0001
   900        0.6688             nan     0.0100   -0.0001
   920        0.6666             nan     0.0100   -0.0001
   940        0.6642             nan     0.0100   -0.0001
   960        0.6620             nan     0.0100   -0.0002
   980        0.6597             nan     0.0100   -0.0002
  1000        0.6575             nan     0.0100   -0.0000
  1020        0.6553             nan     0.0100   -0.0001
  1040        0.6525             nan     0.0100   -0.0000
  1060        0.6501             nan     0.0100   -0.0002
  1080        0.6479             nan     0.0100   -0.0002
  1100        0.6453             nan     0.0100   -0.0000

- Fold10.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2755             nan     0.1000    0.0280
     2        1.2291             nan     0.1000    0.0227
     3        1.1916             nan     0.1000    0.0182
     4        1.1601             nan     0.1000    0.0147
     5        1.1321             nan     0.1000    0.0113
     6        1.1083             nan     0.1000    0.0100
     7        1.0897             nan     0.1000    0.0053
     8        1.0741             nan     0.1000    0.0080
     9        1.0593             nan     0.1000    0.0065
    10        1.0433             nan     0.1000    0.0051
    20        0.9604             nan     0.1000    0.0005
    40        0.8860             nan     0.1000   -0.0000
    60        0.8505             nan     0.1000   -0.0000
    80        0.8267             nan     0.1000   -0.0006
   100        0.8080             nan     0.1000   -0.0003
   120        0.7949             nan     0.1000   -0.0006
   140        0.7860             nan     0.1000   -0.0005
   160        0.7787             nan     0.1000   -0.0006
   180        0.7736             nan     0.1000   -0.0009
   200        0.7661             nan     0.1000   -0.0001
   220        0.7611             nan     0.1000   -0.0008
   240        0.7534             nan     0.1000   -0.0004
   260        0.7491             nan     0.1000   -0.0007
   280        0.7430             nan     0.1000   -0.0008
   300        0.7384             nan     0.1000   -0.0006
   320        0.7361             nan     0.1000   -0.0005
   340        0.7332             nan     0.1000   -0.0010
   360        0.7289             nan     0.1000   -0.0003
   380        0.7257             nan     0.1000   -0.0002
   400        0.7218             nan     0.1000   -0.0004
   420        0.7195             nan     0.1000   -0.0012
   440        0.7164             nan     0.1000   -0.0002
   460        0.7148             nan     0.1000   -0.0008
   480        0.7124             nan     0.1000   -0.0005
   500        0.7095             nan     0.1000   -0.0015
   520        0.7071             nan     0.1000   -0.0007
   540        0.7035             nan     0.1000   -0.0005
   560        0.7008             nan     0.1000   -0.0008
   580        0.6988             nan     0.1000   -0.0005
   600        0.6955             nan     0.1000   -0.0003
   620        0.6946             nan     0.1000   -0.0004
   640        0.6934             nan     0.1000   -0.0009
   660        0.6925             nan     0.1000   -0.0018
   680        0.6897             nan     0.1000   -0.0010
   700        0.6891             nan     0.1000   -0.0011
   720        0.6855             nan     0.1000   -0.0013
   740        0.6833             nan     0.1000   -0.0014
   760        0.6823             nan     0.1000   -0.0014
   780        0.6803             nan     0.1000   -0.0004
   800        0.6779             nan     0.1000   -0.0005
   820        0.6758             nan     0.1000   -0.0007
   840        0.6736             nan     0.1000   -0.0002
   860        0.6715             nan     0.1000   -0.0007
   880        0.6698             nan     0.1000   -0.0007
   900        0.6672             nan     0.1000   -0.0006
   920        0.6663             nan     0.1000   -0.0008
   940        0.6649             nan     0.1000   -0.0011
   960        0.6635             nan     0.1000   -0.0009
   980        0.6620             nan     0.1000   -0.0008
  1000        0.6607             nan     0.1000   -0.0003
  1020        0.6607             nan     0.1000   -0.0011
  1040        0.6591             nan     0.1000   -0.0003
  1060        0.6573             nan     0.1000   -0.0007
  1080        0.6562             nan     0.1000   -0.0010
  1100        0.6548             nan     0.1000   -0.0008

- Fold10.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2652             nan     0.1000    0.0336
     2        1.2109             nan     0.1000    0.0274
     3        1.1668             nan     0.1000    0.0239
     4        1.1284             nan     0.1000    0.0201
     5        1.0954             nan     0.1000    0.0158
     6        1.0619             nan     0.1000    0.0146
     7        1.0367             nan     0.1000    0.0101
     8        1.0129             nan     0.1000    0.0099
     9        0.9944             nan     0.1000    0.0095
    10        0.9775             nan     0.1000    0.0076
    20        0.8814             nan     0.1000    0.0012
    40        0.8099             nan     0.1000   -0.0005
    60        0.7730             nan     0.1000   -0.0013
    80        0.7457             nan     0.1000   -0.0008
   100        0.7252             nan     0.1000   -0.0005
   120        0.7053             nan     0.1000   -0.0016
   140        0.6875             nan     0.1000   -0.0003
   160        0.6744             nan     0.1000   -0.0004
   180        0.6607             nan     0.1000   -0.0014
   200        0.6463             nan     0.1000   -0.0014
   220        0.6349             nan     0.1000   -0.0007
   240        0.6242             nan     0.1000   -0.0016
   260        0.6095             nan     0.1000   -0.0007
   280        0.6003             nan     0.1000   -0.0001
   300        0.5921             nan     0.1000   -0.0013
   320        0.5825             nan     0.1000   -0.0012
   340        0.5750             nan     0.1000   -0.0013
   360        0.5677             nan     0.1000   -0.0008
   380        0.5632             nan     0.1000   -0.0012
   400        0.5560             nan     0.1000   -0.0012
   420        0.5484             nan     0.1000   -0.0010
   440        0.5409             nan     0.1000   -0.0012
   460        0.5328             nan     0.1000   -0.0016
   480        0.5251             nan     0.1000   -0.0005
   500        0.5175             nan     0.1000   -0.0010
   520        0.5109             nan     0.1000   -0.0006
   540        0.5040             nan     0.1000   -0.0016
   560        0.4966             nan     0.1000   -0.0011
   580        0.4910             nan     0.1000   -0.0011
   600        0.4869             nan     0.1000   -0.0013
   620        0.4809             nan     0.1000   -0.0010
   640        0.4769             nan     0.1000   -0.0010
   660        0.4727             nan     0.1000   -0.0012
   680        0.4694             nan     0.1000   -0.0005
   700        0.4647             nan     0.1000   -0.0005
   720        0.4606             nan     0.1000   -0.0007
   740        0.4560             nan     0.1000   -0.0017
   760        0.4546             nan     0.1000   -0.0006
   780        0.4500             nan     0.1000   -0.0006
   800        0.4454             nan     0.1000   -0.0006
   820        0.4419             nan     0.1000   -0.0008
   840        0.4389             nan     0.1000   -0.0008
   860        0.4352             nan     0.1000   -0.0011
   880        0.4326             nan     0.1000   -0.0005
   900        0.4274             nan     0.1000   -0.0005
   920        0.4245             nan     0.1000   -0.0008
   940        0.4218             nan     0.1000   -0.0008
   960        0.4177             nan     0.1000   -0.0008
   980        0.4141             nan     0.1000   -0.0005
  1000        0.4102             nan     0.1000   -0.0003
  1020        0.4077             nan     0.1000   -0.0007
  1040        0.4051             nan     0.1000   -0.0005
  1060        0.4004             nan     0.1000   -0.0005
  1080        0.3979             nan     0.1000   -0.0002
  1100        0.3944             nan     0.1000   -0.0011

- Fold10.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2569             nan     0.1000    0.0366
     2        1.1927             nan     0.1000    0.0313
     3        1.1385             nan     0.1000    0.0254
     4        1.0968             nan     0.1000    0.0203
     5        1.0619             nan     0.1000    0.0156
     6        1.0267             nan     0.1000    0.0154
     7        0.9989             nan     0.1000    0.0117
     8        0.9776             nan     0.1000    0.0107
     9        0.9575             nan     0.1000    0.0088
    10        0.9381             nan     0.1000    0.0065
    20        0.8440             nan     0.1000    0.0017
    40        0.7599             nan     0.1000    0.0005
    60        0.7125             nan     0.1000   -0.0005
    80        0.6792             nan     0.1000   -0.0005
   100        0.6563             nan     0.1000   -0.0005
   120        0.6305             nan     0.1000   -0.0007
   140        0.6112             nan     0.1000   -0.0010
   160        0.5924             nan     0.1000   -0.0006
   180        0.5747             nan     0.1000   -0.0014
   200        0.5599             nan     0.1000   -0.0011
   220        0.5473             nan     0.1000   -0.0013
   240        0.5339             nan     0.1000   -0.0007
   260        0.5193             nan     0.1000   -0.0010
   280        0.5053             nan     0.1000   -0.0004
   300        0.4990             nan     0.1000   -0.0005
   320        0.4889             nan     0.1000   -0.0011
   340        0.4773             nan     0.1000   -0.0015
   360        0.4701             nan     0.1000   -0.0014
   380        0.4584             nan     0.1000   -0.0016
   400        0.4485             nan     0.1000   -0.0014
   420        0.4396             nan     0.1000   -0.0009
   440        0.4324             nan     0.1000   -0.0013
   460        0.4269             nan     0.1000   -0.0009
   480        0.4160             nan     0.1000   -0.0008
   500        0.4077             nan     0.1000   -0.0011
   520        0.4006             nan     0.1000   -0.0014
   540        0.3933             nan     0.1000   -0.0007
   560        0.3879             nan     0.1000   -0.0018
   580        0.3812             nan     0.1000   -0.0017
   600        0.3748             nan     0.1000   -0.0019
   620        0.3685             nan     0.1000   -0.0010
   640        0.3628             nan     0.1000   -0.0008
   660        0.3571             nan     0.1000   -0.0015
   680        0.3516             nan     0.1000   -0.0004
   700        0.3458             nan     0.1000   -0.0009
   720        0.3411             nan     0.1000   -0.0014
   740        0.3366             nan     0.1000   -0.0018
   760        0.3313             nan     0.1000   -0.0012
   780        0.3274             nan     0.1000   -0.0005
   800        0.3243             nan     0.1000   -0.0013
   820        0.3191             nan     0.1000   -0.0009
   840        0.3141             nan     0.1000   -0.0014
   860        0.3114             nan     0.1000   -0.0014
   880        0.3064             nan     0.1000   -0.0008
   900        0.3027             nan     0.1000   -0.0004
   920        0.2989             nan     0.1000   -0.0009
   940        0.2943             nan     0.1000   -0.0007
   960        0.2912             nan     0.1000   -0.0013
   980        0.2882             nan     0.1000   -0.0012
  1000        0.2828             nan     0.1000   -0.0007
  1020        0.2793             nan     0.1000   -0.0009
  1040        0.2767             nan     0.1000   -0.0010
  1060        0.2723             nan     0.1000   -0.0009
  1080        0.2688             nan     0.1000   -0.0006
  1100        0.2648             nan     0.1000   -0.0007

- Fold10.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3258             nan     0.0100    0.0028
     2        1.3199             nan     0.0100    0.0027
     3        1.3150             nan     0.0100    0.0027
     4        1.3097             nan     0.0100    0.0026
     5        1.3045             nan     0.0100    0.0025
     6        1.2993             nan     0.0100    0.0026
     7        1.2947             nan     0.0100    0.0025
     8        1.2897             nan     0.0100    0.0024
     9        1.2844             nan     0.0100    0.0023
    10        1.2798             nan     0.0100    0.0024
    20        1.2359             nan     0.0100    0.0019
    40        1.1688             nan     0.0100    0.0013
    60        1.1224             nan     0.0100    0.0008
    80        1.0865             nan     0.0100    0.0007
   100        1.0575             nan     0.0100    0.0006
   120        1.0340             nan     0.0100    0.0005
   140        1.0137             nan     0.0100    0.0004
   160        0.9966             nan     0.0100    0.0002
   180        0.9820             nan     0.0100    0.0003
   200        0.9679             nan     0.0100    0.0003
   220        0.9550             nan     0.0100    0.0002
   240        0.9442             nan     0.0100    0.0002
   260        0.9350             nan     0.0100    0.0002
   280        0.9262             nan     0.0100    0.0002
   300        0.9179             nan     0.0100   -0.0000
   320        0.9110             nan     0.0100    0.0001
   340        0.9043             nan     0.0100    0.0001
   360        0.8978             nan     0.0100    0.0000
   380        0.8921             nan     0.0100    0.0000
   400        0.8872             nan     0.0100   -0.0000
   420        0.8826             nan     0.0100    0.0000
   440        0.8779             nan     0.0100    0.0000
   460        0.8733             nan     0.0100    0.0000
   480        0.8691             nan     0.0100    0.0001
   500        0.8651             nan     0.0100    0.0000
   520        0.8617             nan     0.0100    0.0000
   540        0.8581             nan     0.0100    0.0001
   560        0.8545             nan     0.0100   -0.0000
   580        0.8512             nan     0.0100    0.0000
   600        0.8480             nan     0.0100    0.0001
   620        0.8451             nan     0.0100    0.0000
   640        0.8421             nan     0.0100   -0.0001
   660        0.8392             nan     0.0100   -0.0000
   680        0.8366             nan     0.0100    0.0001
   700        0.8341             nan     0.0100    0.0000
   720        0.8319             nan     0.0100   -0.0000
   740        0.8297             nan     0.0100   -0.0000
   760        0.8272             nan     0.0100    0.0000
   780        0.8249             nan     0.0100    0.0000
   800        0.8228             nan     0.0100   -0.0000
   820        0.8209             nan     0.0100   -0.0000
   840        0.8188             nan     0.0100   -0.0000
   860        0.8170             nan     0.0100   -0.0001
   880        0.8152             nan     0.0100   -0.0000
   900        0.8136             nan     0.0100   -0.0001
   920        0.8118             nan     0.0100   -0.0000
   940        0.8102             nan     0.0100    0.0000
   960        0.8089             nan     0.0100   -0.0001
   980        0.8074             nan     0.0100   -0.0001
  1000        0.8059             nan     0.0100   -0.0001
  1020        0.8045             nan     0.0100   -0.0000
  1040        0.8032             nan     0.0100   -0.0001
  1060        0.8016             nan     0.0100   -0.0000
  1080        0.8002             nan     0.0100   -0.0000
  1100        0.7988             nan     0.0100   -0.0001

- Fold01.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0033
     2        1.3173             nan     0.0100    0.0037
     3        1.3105             nan     0.0100    0.0032
     4        1.3037             nan     0.0100    0.0032
     5        1.2968             nan     0.0100    0.0033
     6        1.2897             nan     0.0100    0.0032
     7        1.2830             nan     0.0100    0.0033
     8        1.2766             nan     0.0100    0.0032
     9        1.2701             nan     0.0100    0.0029
    10        1.2639             nan     0.0100    0.0028
    20        1.2099             nan     0.0100    0.0025
    40        1.1250             nan     0.0100    0.0018
    60        1.0610             nan     0.0100    0.0013
    80        1.0136             nan     0.0100    0.0010
   100        0.9776             nan     0.0100    0.0006
   120        0.9482             nan     0.0100    0.0005
   140        0.9262             nan     0.0100    0.0003
   160        0.9075             nan     0.0100    0.0003
   180        0.8926             nan     0.0100    0.0001
   200        0.8794             nan     0.0100    0.0001
   220        0.8676             nan     0.0100    0.0001
   240        0.8569             nan     0.0100    0.0001
   260        0.8476             nan     0.0100    0.0001
   280        0.8392             nan     0.0100   -0.0000
   300        0.8316             nan     0.0100   -0.0001
   320        0.8249             nan     0.0100    0.0000
   340        0.8185             nan     0.0100   -0.0000
   360        0.8125             nan     0.0100    0.0000
   380        0.8071             nan     0.0100    0.0000
   400        0.8024             nan     0.0100    0.0001
   420        0.7982             nan     0.0100   -0.0000
   440        0.7937             nan     0.0100   -0.0000
   460        0.7889             nan     0.0100   -0.0000
   480        0.7851             nan     0.0100   -0.0001
   500        0.7813             nan     0.0100   -0.0000
   520        0.7779             nan     0.0100   -0.0001
   540        0.7748             nan     0.0100   -0.0000
   560        0.7715             nan     0.0100   -0.0000
   580        0.7682             nan     0.0100   -0.0002
   600        0.7654             nan     0.0100   -0.0000
   620        0.7628             nan     0.0100   -0.0001
   640        0.7599             nan     0.0100    0.0000
   660        0.7568             nan     0.0100   -0.0000
   680        0.7545             nan     0.0100   -0.0000
   700        0.7518             nan     0.0100   -0.0001
   720        0.7492             nan     0.0100   -0.0001
   740        0.7465             nan     0.0100   -0.0002
   760        0.7442             nan     0.0100   -0.0001
   780        0.7416             nan     0.0100   -0.0001
   800        0.7391             nan     0.0100   -0.0001
   820        0.7370             nan     0.0100   -0.0000
   840        0.7348             nan     0.0100   -0.0001
   860        0.7331             nan     0.0100   -0.0001
   880        0.7310             nan     0.0100   -0.0001
   900        0.7291             nan     0.0100   -0.0001
   920        0.7275             nan     0.0100   -0.0000
   940        0.7258             nan     0.0100   -0.0001
   960        0.7238             nan     0.0100   -0.0001
   980        0.7217             nan     0.0100    0.0000
  1000        0.7198             nan     0.0100   -0.0001
  1020        0.7180             nan     0.0100   -0.0000
  1040        0.7159             nan     0.0100   -0.0002
  1060        0.7144             nan     0.0100   -0.0001
  1080        0.7126             nan     0.0100   -0.0000
  1100        0.7110             nan     0.0100   -0.0001

- Fold01.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3230             nan     0.0100    0.0038
     2        1.3155             nan     0.0100    0.0035
     3        1.3078             nan     0.0100    0.0038
     4        1.2999             nan     0.0100    0.0037
     5        1.2927             nan     0.0100    0.0036
     6        1.2861             nan     0.0100    0.0030
     7        1.2789             nan     0.0100    0.0032
     8        1.2714             nan     0.0100    0.0034
     9        1.2648             nan     0.0100    0.0030
    10        1.2582             nan     0.0100    0.0032
    20        1.1971             nan     0.0100    0.0027
    40        1.1041             nan     0.0100    0.0019
    60        1.0354             nan     0.0100    0.0013
    80        0.9834             nan     0.0100    0.0008
   100        0.9435             nan     0.0100    0.0005
   120        0.9145             nan     0.0100    0.0005
   140        0.8912             nan     0.0100    0.0005
   160        0.8725             nan     0.0100    0.0002
   180        0.8560             nan     0.0100    0.0003
   200        0.8423             nan     0.0100    0.0001
   220        0.8304             nan     0.0100    0.0000
   240        0.8192             nan     0.0100    0.0002
   260        0.8088             nan     0.0100    0.0001
   280        0.8000             nan     0.0100   -0.0000
   300        0.7922             nan     0.0100    0.0001
   320        0.7857             nan     0.0100    0.0001
   340        0.7789             nan     0.0100    0.0000
   360        0.7730             nan     0.0100   -0.0002
   380        0.7673             nan     0.0100   -0.0001
   400        0.7616             nan     0.0100    0.0001
   420        0.7564             nan     0.0100    0.0000
   440        0.7513             nan     0.0100    0.0000
   460        0.7469             nan     0.0100   -0.0000
   480        0.7426             nan     0.0100   -0.0000
   500        0.7390             nan     0.0100   -0.0002
   520        0.7351             nan     0.0100   -0.0001
   540        0.7313             nan     0.0100   -0.0000
   560        0.7277             nan     0.0100    0.0000
   580        0.7234             nan     0.0100   -0.0002
   600        0.7201             nan     0.0100   -0.0000
   620        0.7167             nan     0.0100   -0.0002
   640        0.7134             nan     0.0100   -0.0002
   660        0.7103             nan     0.0100   -0.0001
   680        0.7070             nan     0.0100   -0.0000
   700        0.7038             nan     0.0100   -0.0000
   720        0.7004             nan     0.0100   -0.0001
   740        0.6973             nan     0.0100   -0.0001
   760        0.6943             nan     0.0100   -0.0001
   780        0.6911             nan     0.0100   -0.0001
   800        0.6886             nan     0.0100   -0.0001
   820        0.6860             nan     0.0100   -0.0001
   840        0.6830             nan     0.0100   -0.0001
   860        0.6807             nan     0.0100   -0.0001
   880        0.6781             nan     0.0100   -0.0001
   900        0.6755             nan     0.0100   -0.0001
   920        0.6727             nan     0.0100   -0.0001
   940        0.6704             nan     0.0100   -0.0001
   960        0.6677             nan     0.0100   -0.0001
   980        0.6655             nan     0.0100   -0.0001
  1000        0.6629             nan     0.0100   -0.0000
  1020        0.6601             nan     0.0100   -0.0000
  1040        0.6578             nan     0.0100   -0.0002
  1060        0.6557             nan     0.0100   -0.0002
  1080        0.6535             nan     0.0100   -0.0002
  1100        0.6514             nan     0.0100   -0.0001

- Fold01.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2761             nan     0.1000    0.0264
     2        1.2333             nan     0.1000    0.0224
     3        1.1978             nan     0.1000    0.0183
     4        1.1618             nan     0.1000    0.0130
     5        1.1361             nan     0.1000    0.0117
     6        1.1191             nan     0.1000    0.0074
     7        1.0972             nan     0.1000    0.0096
     8        1.0806             nan     0.1000    0.0080
     9        1.0670             nan     0.1000    0.0064
    10        1.0518             nan     0.1000    0.0062
    20        0.9638             nan     0.1000    0.0031
    40        0.8833             nan     0.1000    0.0010
    60        0.8459             nan     0.1000    0.0000
    80        0.8210             nan     0.1000   -0.0001
   100        0.8051             nan     0.1000   -0.0004
   120        0.7931             nan     0.1000   -0.0008
   140        0.7843             nan     0.1000   -0.0003
   160        0.7747             nan     0.1000   -0.0002
   180        0.7685             nan     0.1000   -0.0005
   200        0.7620             nan     0.1000   -0.0007
   220        0.7568             nan     0.1000   -0.0005
   240        0.7528             nan     0.1000   -0.0007
   260        0.7474             nan     0.1000   -0.0005
   280        0.7448             nan     0.1000   -0.0019
   300        0.7428             nan     0.1000   -0.0006
   320        0.7374             nan     0.1000   -0.0000
   340        0.7338             nan     0.1000   -0.0010
   360        0.7312             nan     0.1000   -0.0005
   380        0.7289             nan     0.1000   -0.0012
   400        0.7254             nan     0.1000   -0.0002
   420        0.7222             nan     0.1000   -0.0010
   440        0.7179             nan     0.1000   -0.0004
   460        0.7152             nan     0.1000   -0.0003
   480        0.7118             nan     0.1000   -0.0006
   500        0.7107             nan     0.1000   -0.0008
   520        0.7099             nan     0.1000   -0.0005
   540        0.7074             nan     0.1000   -0.0003
   560        0.7060             nan     0.1000   -0.0012
   580        0.7048             nan     0.1000   -0.0009
   600        0.7026             nan     0.1000   -0.0007
   620        0.7000             nan     0.1000   -0.0008
   640        0.6985             nan     0.1000   -0.0008
   660        0.6951             nan     0.1000   -0.0005
   680        0.6931             nan     0.1000   -0.0005
   700        0.6909             nan     0.1000   -0.0004
   720        0.6885             nan     0.1000   -0.0006
   740        0.6871             nan     0.1000   -0.0010
   760        0.6862             nan     0.1000   -0.0004
   780        0.6839             nan     0.1000   -0.0010
   800        0.6834             nan     0.1000   -0.0009
   820        0.6822             nan     0.1000   -0.0010
   840        0.6813             nan     0.1000   -0.0010
   860        0.6793             nan     0.1000   -0.0013
   880        0.6781             nan     0.1000   -0.0007
   900        0.6752             nan     0.1000   -0.0004
   920        0.6732             nan     0.1000   -0.0004
   940        0.6728             nan     0.1000   -0.0009
   960        0.6712             nan     0.1000   -0.0005
   980        0.6707             nan     0.1000   -0.0013
  1000        0.6685             nan     0.1000   -0.0009
  1020        0.6683             nan     0.1000   -0.0010
  1040        0.6678             nan     0.1000   -0.0007
  1060        0.6655             nan     0.1000   -0.0004
  1080        0.6641             nan     0.1000   -0.0010
  1100        0.6627             nan     0.1000   -0.0006

- Fold01.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2623             nan     0.1000    0.0361
     2        1.2045             nan     0.1000    0.0272
     3        1.1608             nan     0.1000    0.0219
     4        1.1168             nan     0.1000    0.0195
     5        1.0881             nan     0.1000    0.0142
     6        1.0572             nan     0.1000    0.0153
     7        1.0321             nan     0.1000    0.0113
     8        1.0082             nan     0.1000    0.0087
     9        0.9859             nan     0.1000    0.0089
    10        0.9682             nan     0.1000    0.0064
    20        0.8688             nan     0.1000    0.0007
    40        0.8036             nan     0.1000   -0.0007
    60        0.7648             nan     0.1000   -0.0001
    80        0.7421             nan     0.1000   -0.0006
   100        0.7195             nan     0.1000   -0.0019
   120        0.6992             nan     0.1000   -0.0006
   140        0.6849             nan     0.1000   -0.0006
   160        0.6725             nan     0.1000   -0.0002
   180        0.6592             nan     0.1000   -0.0005
   200        0.6478             nan     0.1000   -0.0006
   220        0.6376             nan     0.1000   -0.0005
   240        0.6259             nan     0.1000   -0.0009
   260        0.6183             nan     0.1000   -0.0006
   280        0.6123             nan     0.1000   -0.0006
   300        0.6024             nan     0.1000   -0.0006
   320        0.5965             nan     0.1000   -0.0007
   340        0.5881             nan     0.1000   -0.0008
   360        0.5825             nan     0.1000   -0.0015
   380        0.5759             nan     0.1000   -0.0010
   400        0.5687             nan     0.1000   -0.0010
   420        0.5622             nan     0.1000   -0.0010
   440        0.5561             nan     0.1000   -0.0010
   460        0.5501             nan     0.1000   -0.0012
   480        0.5452             nan     0.1000   -0.0013
   500        0.5398             nan     0.1000   -0.0007
   520        0.5353             nan     0.1000   -0.0006
   540        0.5314             nan     0.1000   -0.0012
   560        0.5250             nan     0.1000   -0.0003
   580        0.5168             nan     0.1000   -0.0008
   600        0.5108             nan     0.1000   -0.0012
   620        0.5053             nan     0.1000   -0.0009
   640        0.4982             nan     0.1000   -0.0016
   660        0.4941             nan     0.1000   -0.0009
   680        0.4894             nan     0.1000   -0.0010
   700        0.4840             nan     0.1000   -0.0011
   720        0.4796             nan     0.1000   -0.0004
   740        0.4767             nan     0.1000   -0.0007
   760        0.4713             nan     0.1000   -0.0007
   780        0.4690             nan     0.1000   -0.0009
   800        0.4646             nan     0.1000   -0.0007
   820        0.4595             nan     0.1000   -0.0008
   840        0.4555             nan     0.1000   -0.0010
   860        0.4512             nan     0.1000   -0.0010
   880        0.4463             nan     0.1000   -0.0008
   900        0.4424             nan     0.1000   -0.0005
   920        0.4369             nan     0.1000   -0.0009
   940        0.4337             nan     0.1000   -0.0003
   960        0.4298             nan     0.1000   -0.0009
   980        0.4249             nan     0.1000   -0.0009
  1000        0.4214             nan     0.1000   -0.0007
  1020        0.4187             nan     0.1000   -0.0011
  1040        0.4163             nan     0.1000   -0.0009
  1060        0.4147             nan     0.1000   -0.0009
  1080        0.4117             nan     0.1000   -0.0010
  1100        0.4080             nan     0.1000   -0.0003

- Fold01.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2529             nan     0.1000    0.0365
     2        1.1944             nan     0.1000    0.0269
     3        1.1455             nan     0.1000    0.0254
     4        1.1054             nan     0.1000    0.0175
     5        1.0676             nan     0.1000    0.0170
     6        1.0322             nan     0.1000    0.0164
     7        1.0028             nan     0.1000    0.0132
     8        0.9790             nan     0.1000    0.0098
     9        0.9578             nan     0.1000    0.0087
    10        0.9412             nan     0.1000    0.0059
    20        0.8436             nan     0.1000    0.0014
    40        0.7668             nan     0.1000    0.0011
    60        0.7249             nan     0.1000   -0.0022
    80        0.6937             nan     0.1000   -0.0019
   100        0.6636             nan     0.1000   -0.0006
   120        0.6421             nan     0.1000   -0.0008
   140        0.6163             nan     0.1000   -0.0013
   160        0.5996             nan     0.1000   -0.0029
   180        0.5836             nan     0.1000   -0.0010
   200        0.5675             nan     0.1000   -0.0005
   220        0.5530             nan     0.1000   -0.0020
   240        0.5364             nan     0.1000   -0.0011
   260        0.5249             nan     0.1000   -0.0030
   280        0.5160             nan     0.1000   -0.0012
   300        0.5023             nan     0.1000   -0.0015
   320        0.4924             nan     0.1000   -0.0003
   340        0.4823             nan     0.1000   -0.0012
   360        0.4713             nan     0.1000   -0.0020
   380        0.4611             nan     0.1000   -0.0002
   400        0.4511             nan     0.1000   -0.0011
   420        0.4442             nan     0.1000   -0.0015
   440        0.4348             nan     0.1000   -0.0023
   460        0.4262             nan     0.1000   -0.0013
   480        0.4151             nan     0.1000   -0.0011
   500        0.4093             nan     0.1000   -0.0013
   520        0.4031             nan     0.1000   -0.0012
   540        0.3978             nan     0.1000   -0.0008
   560        0.3929             nan     0.1000   -0.0009
   580        0.3848             nan     0.1000   -0.0012
   600        0.3770             nan     0.1000   -0.0009
   620        0.3719             nan     0.1000   -0.0021
   640        0.3666             nan     0.1000   -0.0014
   660        0.3609             nan     0.1000   -0.0005
   680        0.3551             nan     0.1000   -0.0013
   700        0.3502             nan     0.1000   -0.0008
   720        0.3449             nan     0.1000   -0.0010
   740        0.3394             nan     0.1000   -0.0006
   760        0.3329             nan     0.1000   -0.0004
   780        0.3285             nan     0.1000   -0.0005
   800        0.3238             nan     0.1000   -0.0008
   820        0.3188             nan     0.1000   -0.0010
   840        0.3142             nan     0.1000   -0.0010
   860        0.3105             nan     0.1000   -0.0009
   880        0.3074             nan     0.1000   -0.0007
   900        0.3030             nan     0.1000   -0.0011
   920        0.2988             nan     0.1000   -0.0008
   940        0.2955             nan     0.1000   -0.0006
   960        0.2927             nan     0.1000   -0.0007
   980        0.2880             nan     0.1000   -0.0003
  1000        0.2843             nan     0.1000   -0.0013
  1020        0.2815             nan     0.1000   -0.0004
  1040        0.2784             nan     0.1000   -0.0006
  1060        0.2731             nan     0.1000   -0.0007
  1080        0.2702             nan     0.1000   -0.0010
  1100        0.2672             nan     0.1000   -0.0004

- Fold01.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3266             nan     0.0100    0.0028
     2        1.3213             nan     0.0100    0.0027
     3        1.3154             nan     0.0100    0.0028
     4        1.3102             nan     0.0100    0.0025
     5        1.3047             nan     0.0100    0.0027
     6        1.3004             nan     0.0100    0.0025
     7        1.2947             nan     0.0100    0.0026
     8        1.2899             nan     0.0100    0.0025
     9        1.2848             nan     0.0100    0.0025
    10        1.2801             nan     0.0100    0.0022
    20        1.2344             nan     0.0100    0.0019
    40        1.1660             nan     0.0100    0.0014
    60        1.1209             nan     0.0100    0.0010
    80        1.0862             nan     0.0100    0.0008
   100        1.0577             nan     0.0100    0.0006
   120        1.0338             nan     0.0100    0.0004
   140        1.0133             nan     0.0100    0.0004
   160        0.9954             nan     0.0100    0.0003
   180        0.9808             nan     0.0100    0.0003
   200        0.9677             nan     0.0100    0.0003
   220        0.9560             nan     0.0100    0.0002
   240        0.9457             nan     0.0100    0.0003
   260        0.9362             nan     0.0100    0.0002
   280        0.9278             nan     0.0100    0.0001
   300        0.9200             nan     0.0100    0.0001
   320        0.9126             nan     0.0100    0.0001
   340        0.9059             nan     0.0100    0.0001
   360        0.8998             nan     0.0100    0.0001
   380        0.8940             nan     0.0100    0.0000
   400        0.8890             nan     0.0100    0.0001
   420        0.8843             nan     0.0100   -0.0000
   440        0.8798             nan     0.0100    0.0000
   460        0.8754             nan     0.0100    0.0000
   480        0.8711             nan     0.0100    0.0000
   500        0.8671             nan     0.0100    0.0001
   520        0.8636             nan     0.0100    0.0000
   540        0.8601             nan     0.0100   -0.0000
   560        0.8570             nan     0.0100    0.0001
   580        0.8540             nan     0.0100    0.0000
   600        0.8508             nan     0.0100    0.0001
   620        0.8480             nan     0.0100    0.0000
   640        0.8451             nan     0.0100   -0.0000
   660        0.8427             nan     0.0100   -0.0001
   680        0.8401             nan     0.0100   -0.0001
   700        0.8374             nan     0.0100    0.0000
   720        0.8351             nan     0.0100   -0.0001
   740        0.8329             nan     0.0100   -0.0001
   760        0.8306             nan     0.0100    0.0000
   780        0.8284             nan     0.0100    0.0000
   800        0.8264             nan     0.0100    0.0000
   820        0.8243             nan     0.0100   -0.0000
   840        0.8226             nan     0.0100    0.0000
   860        0.8208             nan     0.0100   -0.0001
   880        0.8187             nan     0.0100    0.0000
   900        0.8171             nan     0.0100   -0.0001
   920        0.8156             nan     0.0100   -0.0000
   940        0.8141             nan     0.0100   -0.0000
   960        0.8126             nan     0.0100   -0.0001
   980        0.8112             nan     0.0100   -0.0000
  1000        0.8097             nan     0.0100   -0.0000
  1020        0.8083             nan     0.0100   -0.0000
  1040        0.8068             nan     0.0100   -0.0000
  1060        0.8054             nan     0.0100   -0.0000
  1080        0.8041             nan     0.0100   -0.0000
  1100        0.8030             nan     0.0100   -0.0001

- Fold02.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0033
     2        1.3176             nan     0.0100    0.0034
     3        1.3106             nan     0.0100    0.0033
     4        1.3036             nan     0.0100    0.0034
     5        1.2968             nan     0.0100    0.0033
     6        1.2899             nan     0.0100    0.0033
     7        1.2836             nan     0.0100    0.0031
     8        1.2770             nan     0.0100    0.0030
     9        1.2710             nan     0.0100    0.0031
    10        1.2648             nan     0.0100    0.0030
    20        1.2098             nan     0.0100    0.0023
    40        1.1244             nan     0.0100    0.0017
    60        1.0618             nan     0.0100    0.0011
    80        1.0148             nan     0.0100    0.0007
   100        0.9805             nan     0.0100    0.0007
   120        0.9528             nan     0.0100    0.0005
   140        0.9305             nan     0.0100    0.0004
   160        0.9122             nan     0.0100    0.0002
   180        0.8988             nan     0.0100    0.0000
   200        0.8856             nan     0.0100    0.0002
   220        0.8743             nan     0.0100    0.0002
   240        0.8637             nan     0.0100    0.0001
   260        0.8547             nan     0.0100    0.0001
   280        0.8457             nan     0.0100   -0.0000
   300        0.8379             nan     0.0100    0.0000
   320        0.8303             nan     0.0100    0.0001
   340        0.8241             nan     0.0100    0.0001
   360        0.8181             nan     0.0100   -0.0000
   380        0.8124             nan     0.0100    0.0002
   400        0.8072             nan     0.0100    0.0001
   420        0.8024             nan     0.0100   -0.0001
   440        0.7973             nan     0.0100    0.0000
   460        0.7930             nan     0.0100    0.0000
   480        0.7887             nan     0.0100   -0.0000
   500        0.7847             nan     0.0100   -0.0001
   520        0.7811             nan     0.0100   -0.0001
   540        0.7777             nan     0.0100   -0.0001
   560        0.7738             nan     0.0100    0.0000
   580        0.7703             nan     0.0100   -0.0000
   600        0.7671             nan     0.0100    0.0001
   620        0.7642             nan     0.0100   -0.0000
   640        0.7606             nan     0.0100    0.0001
   660        0.7576             nan     0.0100   -0.0001
   680        0.7549             nan     0.0100    0.0000
   700        0.7525             nan     0.0100   -0.0001
   720        0.7498             nan     0.0100    0.0000
   740        0.7474             nan     0.0100   -0.0001
   760        0.7449             nan     0.0100   -0.0000
   780        0.7425             nan     0.0100   -0.0001
   800        0.7400             nan     0.0100   -0.0000
   820        0.7380             nan     0.0100   -0.0002
   840        0.7352             nan     0.0100   -0.0001
   860        0.7329             nan     0.0100   -0.0001
   880        0.7309             nan     0.0100   -0.0001
   900        0.7287             nan     0.0100   -0.0001
   920        0.7264             nan     0.0100   -0.0001
   940        0.7241             nan     0.0100   -0.0001
   960        0.7218             nan     0.0100   -0.0000
   980        0.7202             nan     0.0100   -0.0001
  1000        0.7183             nan     0.0100   -0.0001
  1020        0.7164             nan     0.0100   -0.0001
  1040        0.7148             nan     0.0100   -0.0000
  1060        0.7127             nan     0.0100   -0.0001
  1080        0.7109             nan     0.0100   -0.0001
  1100        0.7087             nan     0.0100   -0.0000

- Fold02.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3240             nan     0.0100    0.0037
     2        1.3161             nan     0.0100    0.0035
     3        1.3082             nan     0.0100    0.0039
     4        1.3017             nan     0.0100    0.0033
     5        1.2946             nan     0.0100    0.0037
     6        1.2871             nan     0.0100    0.0034
     7        1.2805             nan     0.0100    0.0034
     8        1.2735             nan     0.0100    0.0033
     9        1.2670             nan     0.0100    0.0030
    10        1.2599             nan     0.0100    0.0034
    20        1.1993             nan     0.0100    0.0027
    40        1.1043             nan     0.0100    0.0018
    60        1.0358             nan     0.0100    0.0015
    80        0.9863             nan     0.0100    0.0010
   100        0.9491             nan     0.0100    0.0004
   120        0.9191             nan     0.0100    0.0004
   140        0.8950             nan     0.0100    0.0003
   160        0.8755             nan     0.0100    0.0004
   180        0.8594             nan     0.0100    0.0002
   200        0.8454             nan     0.0100    0.0000
   220        0.8332             nan     0.0100    0.0001
   240        0.8222             nan     0.0100    0.0000
   260        0.8123             nan     0.0100    0.0001
   280        0.8033             nan     0.0100    0.0002
   300        0.7954             nan     0.0100   -0.0001
   320        0.7880             nan     0.0100   -0.0000
   340        0.7811             nan     0.0100   -0.0000
   360        0.7745             nan     0.0100    0.0000
   380        0.7685             nan     0.0100   -0.0001
   400        0.7631             nan     0.0100   -0.0001
   420        0.7576             nan     0.0100    0.0000
   440        0.7524             nan     0.0100   -0.0001
   460        0.7473             nan     0.0100   -0.0002
   480        0.7433             nan     0.0100   -0.0001
   500        0.7398             nan     0.0100   -0.0001
   520        0.7352             nan     0.0100    0.0000
   540        0.7310             nan     0.0100   -0.0000
   560        0.7269             nan     0.0100   -0.0000
   580        0.7232             nan     0.0100   -0.0001
   600        0.7187             nan     0.0100   -0.0002
   620        0.7151             nan     0.0100   -0.0001
   640        0.7114             nan     0.0100   -0.0000
   660        0.7076             nan     0.0100   -0.0002
   680        0.7043             nan     0.0100   -0.0000
   700        0.7009             nan     0.0100   -0.0000
   720        0.6979             nan     0.0100   -0.0002
   740        0.6945             nan     0.0100   -0.0001
   760        0.6912             nan     0.0100   -0.0001
   780        0.6884             nan     0.0100   -0.0001
   800        0.6852             nan     0.0100   -0.0001
   820        0.6831             nan     0.0100   -0.0000
   840        0.6805             nan     0.0100   -0.0001
   860        0.6773             nan     0.0100   -0.0001
   880        0.6746             nan     0.0100   -0.0001
   900        0.6721             nan     0.0100   -0.0001
   920        0.6695             nan     0.0100   -0.0002
   940        0.6667             nan     0.0100   -0.0001
   960        0.6643             nan     0.0100   -0.0002
   980        0.6621             nan     0.0100   -0.0001
  1000        0.6591             nan     0.0100   -0.0001
  1020        0.6566             nan     0.0100   -0.0001
  1040        0.6541             nan     0.0100   -0.0002
  1060        0.6513             nan     0.0100   -0.0001
  1080        0.6486             nan     0.0100   -0.0002
  1100        0.6461             nan     0.0100   -0.0001

- Fold02.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2745             nan     0.1000    0.0280
     2        1.2283             nan     0.1000    0.0224
     3        1.1940             nan     0.1000    0.0179
     4        1.1637             nan     0.1000    0.0152
     5        1.1388             nan     0.1000    0.0123
     6        1.1156             nan     0.1000    0.0101
     7        1.0995             nan     0.1000    0.0047
     8        1.0847             nan     0.1000    0.0071
     9        1.0674             nan     0.1000    0.0080
    10        1.0545             nan     0.1000    0.0063
    20        0.9663             nan     0.1000    0.0024
    40        0.8897             nan     0.1000    0.0007
    60        0.8528             nan     0.1000    0.0005
    80        0.8264             nan     0.1000   -0.0006
   100        0.8099             nan     0.1000   -0.0000
   120        0.7960             nan     0.1000   -0.0007
   140        0.7860             nan     0.1000   -0.0003
   160        0.7762             nan     0.1000   -0.0004
   180        0.7705             nan     0.1000   -0.0007
   200        0.7637             nan     0.1000   -0.0008
   220        0.7568             nan     0.1000   -0.0019
   240        0.7519             nan     0.1000   -0.0013
   260        0.7474             nan     0.1000   -0.0010
   280        0.7426             nan     0.1000   -0.0007
   300        0.7391             nan     0.1000   -0.0004
   320        0.7345             nan     0.1000   -0.0009
   340        0.7327             nan     0.1000   -0.0009
   360        0.7284             nan     0.1000   -0.0010
   380        0.7249             nan     0.1000   -0.0006
   400        0.7208             nan     0.1000   -0.0006
   420        0.7169             nan     0.1000   -0.0013
   440        0.7139             nan     0.1000   -0.0013
   460        0.7114             nan     0.1000   -0.0005
   480        0.7084             nan     0.1000   -0.0003
   500        0.7067             nan     0.1000   -0.0009
   520        0.7053             nan     0.1000   -0.0009
   540        0.7025             nan     0.1000   -0.0007
   560        0.7001             nan     0.1000   -0.0006
   580        0.6974             nan     0.1000   -0.0007
   600        0.6946             nan     0.1000   -0.0014
   620        0.6930             nan     0.1000   -0.0009
   640        0.6897             nan     0.1000   -0.0011
   660        0.6876             nan     0.1000   -0.0013
   680        0.6852             nan     0.1000   -0.0012
   700        0.6838             nan     0.1000   -0.0011
   720        0.6830             nan     0.1000   -0.0007
   740        0.6812             nan     0.1000   -0.0007
   760        0.6795             nan     0.1000   -0.0008
   780        0.6777             nan     0.1000   -0.0011
   800        0.6768             nan     0.1000   -0.0006
   820        0.6750             nan     0.1000   -0.0003
   840        0.6740             nan     0.1000   -0.0008
   860        0.6733             nan     0.1000   -0.0005
   880        0.6726             nan     0.1000   -0.0009
   900        0.6705             nan     0.1000   -0.0018
   920        0.6696             nan     0.1000   -0.0016
   940        0.6679             nan     0.1000   -0.0008
   960        0.6674             nan     0.1000   -0.0008
   980        0.6663             nan     0.1000   -0.0009
  1000        0.6664             nan     0.1000   -0.0011
  1020        0.6639             nan     0.1000   -0.0008
  1040        0.6631             nan     0.1000   -0.0007
  1060        0.6625             nan     0.1000   -0.0012
  1080        0.6610             nan     0.1000   -0.0011
  1100        0.6592             nan     0.1000   -0.0009

- Fold02.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2637             nan     0.1000    0.0349
     2        1.2098             nan     0.1000    0.0292
     3        1.1585             nan     0.1000    0.0255
     4        1.1184             nan     0.1000    0.0195
     5        1.0850             nan     0.1000    0.0159
     6        1.0561             nan     0.1000    0.0138
     7        1.0298             nan     0.1000    0.0103
     8        1.0064             nan     0.1000    0.0101
     9        0.9875             nan     0.1000    0.0085
    10        0.9704             nan     0.1000    0.0066
    20        0.8762             nan     0.1000    0.0019
    40        0.7991             nan     0.1000   -0.0005
    60        0.7582             nan     0.1000   -0.0006
    80        0.7369             nan     0.1000   -0.0008
   100        0.7158             nan     0.1000   -0.0002
   120        0.6947             nan     0.1000   -0.0003
   140        0.6791             nan     0.1000   -0.0007
   160        0.6693             nan     0.1000   -0.0011
   180        0.6550             nan     0.1000   -0.0005
   200        0.6418             nan     0.1000   -0.0006
   220        0.6279             nan     0.1000   -0.0017
   240        0.6163             nan     0.1000   -0.0018
   260        0.6085             nan     0.1000   -0.0005
   280        0.5996             nan     0.1000   -0.0012
   300        0.5916             nan     0.1000   -0.0016
   320        0.5836             nan     0.1000   -0.0017
   340        0.5745             nan     0.1000   -0.0009
   360        0.5657             nan     0.1000   -0.0004
   380        0.5595             nan     0.1000   -0.0016
   400        0.5501             nan     0.1000   -0.0013
   420        0.5436             nan     0.1000   -0.0008
   440        0.5368             nan     0.1000   -0.0008
   460        0.5301             nan     0.1000   -0.0002
   480        0.5240             nan     0.1000   -0.0011
   500        0.5190             nan     0.1000   -0.0007
   520        0.5125             nan     0.1000   -0.0006
   540        0.5074             nan     0.1000   -0.0008
   560        0.5021             nan     0.1000   -0.0004
   580        0.4970             nan     0.1000   -0.0007
   600        0.4916             nan     0.1000   -0.0003
   620        0.4876             nan     0.1000   -0.0007
   640        0.4824             nan     0.1000   -0.0008
   660        0.4768             nan     0.1000   -0.0011
   680        0.4735             nan     0.1000   -0.0011
   700        0.4687             nan     0.1000   -0.0007
   720        0.4642             nan     0.1000   -0.0017
   740        0.4585             nan     0.1000   -0.0008
   760        0.4542             nan     0.1000   -0.0010
   780        0.4509             nan     0.1000   -0.0011
   800        0.4466             nan     0.1000   -0.0005
   820        0.4435             nan     0.1000   -0.0008
   840        0.4398             nan     0.1000   -0.0006
   860        0.4363             nan     0.1000   -0.0013
   880        0.4331             nan     0.1000   -0.0006
   900        0.4291             nan     0.1000   -0.0006
   920        0.4260             nan     0.1000   -0.0005
   940        0.4221             nan     0.1000   -0.0015
   960        0.4166             nan     0.1000   -0.0010
   980        0.4122             nan     0.1000   -0.0001
  1000        0.4094             nan     0.1000   -0.0008
  1020        0.4062             nan     0.1000   -0.0006
  1040        0.4027             nan     0.1000   -0.0009
  1060        0.3990             nan     0.1000   -0.0014
  1080        0.3954             nan     0.1000   -0.0009
  1100        0.3925             nan     0.1000   -0.0008

- Fold02.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2600             nan     0.1000    0.0403
     2        1.1966             nan     0.1000    0.0295
     3        1.1419             nan     0.1000    0.0257
     4        1.0978             nan     0.1000    0.0216
     5        1.0621             nan     0.1000    0.0168
     6        1.0325             nan     0.1000    0.0136
     7        1.0095             nan     0.1000    0.0108
     8        0.9851             nan     0.1000    0.0119
     9        0.9640             nan     0.1000    0.0093
    10        0.9459             nan     0.1000    0.0076
    20        0.8508             nan     0.1000    0.0024
    40        0.7681             nan     0.1000   -0.0001
    60        0.7195             nan     0.1000   -0.0005
    80        0.6864             nan     0.1000   -0.0011
   100        0.6627             nan     0.1000   -0.0010
   120        0.6385             nan     0.1000   -0.0006
   140        0.6172             nan     0.1000   -0.0014
   160        0.5984             nan     0.1000   -0.0009
   180        0.5790             nan     0.1000   -0.0012
   200        0.5607             nan     0.1000   -0.0013
   220        0.5486             nan     0.1000   -0.0011
   240        0.5328             nan     0.1000   -0.0004
   260        0.5165             nan     0.1000   -0.0012
   280        0.5049             nan     0.1000   -0.0015
   300        0.4945             nan     0.1000   -0.0016
   320        0.4835             nan     0.1000   -0.0020
   340        0.4755             nan     0.1000   -0.0013
   360        0.4656             nan     0.1000   -0.0011
   380        0.4555             nan     0.1000   -0.0009
   400        0.4459             nan     0.1000   -0.0007
   420        0.4393             nan     0.1000   -0.0008
   440        0.4311             nan     0.1000   -0.0011
   460        0.4224             nan     0.1000   -0.0010
   480        0.4137             nan     0.1000   -0.0014
   500        0.4081             nan     0.1000   -0.0009
   520        0.4014             nan     0.1000   -0.0010
   540        0.3928             nan     0.1000   -0.0019
   560        0.3883             nan     0.1000   -0.0012
   580        0.3807             nan     0.1000   -0.0008
   600        0.3750             nan     0.1000   -0.0013
   620        0.3685             nan     0.1000   -0.0008
   640        0.3630             nan     0.1000   -0.0007
   660        0.3580             nan     0.1000   -0.0007
   680        0.3535             nan     0.1000   -0.0013
   700        0.3477             nan     0.1000   -0.0006
   720        0.3442             nan     0.1000   -0.0012
   740        0.3366             nan     0.1000   -0.0008
   760        0.3322             nan     0.1000   -0.0010
   780        0.3260             nan     0.1000   -0.0005
   800        0.3227             nan     0.1000   -0.0008
   820        0.3195             nan     0.1000   -0.0010
   840        0.3142             nan     0.1000   -0.0010
   860        0.3092             nan     0.1000   -0.0009
   880        0.3045             nan     0.1000   -0.0005
   900        0.3002             nan     0.1000   -0.0005
   920        0.2981             nan     0.1000   -0.0011
   940        0.2934             nan     0.1000   -0.0013
   960        0.2888             nan     0.1000   -0.0007
   980        0.2844             nan     0.1000   -0.0006
  1000        0.2803             nan     0.1000   -0.0008
  1020        0.2775             nan     0.1000   -0.0010
  1040        0.2745             nan     0.1000   -0.0010
  1060        0.2707             nan     0.1000   -0.0012
  1080        0.2674             nan     0.1000   -0.0008
  1100        0.2643             nan     0.1000   -0.0009

- Fold02.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3257             nan     0.0100    0.0030
     2        1.3199             nan     0.0100    0.0028
     3        1.3139             nan     0.0100    0.0029
     4        1.3083             nan     0.0100    0.0027
     5        1.3032             nan     0.0100    0.0027
     6        1.2972             nan     0.0100    0.0025
     7        1.2916             nan     0.0100    0.0024
     8        1.2861             nan     0.0100    0.0023
     9        1.2811             nan     0.0100    0.0025
    10        1.2764             nan     0.0100    0.0024
    20        1.2320             nan     0.0100    0.0020
    40        1.1657             nan     0.0100    0.0014
    60        1.1192             nan     0.0100    0.0010
    80        1.0830             nan     0.0100    0.0007
   100        1.0520             nan     0.0100    0.0005
   120        1.0268             nan     0.0100    0.0004
   140        1.0058             nan     0.0100    0.0004
   160        0.9881             nan     0.0100    0.0003
   180        0.9723             nan     0.0100    0.0002
   200        0.9587             nan     0.0100    0.0003
   220        0.9474             nan     0.0100    0.0000
   240        0.9368             nan     0.0100    0.0002
   260        0.9275             nan     0.0100    0.0001
   280        0.9193             nan     0.0100   -0.0000
   300        0.9121             nan     0.0100    0.0001
   320        0.9055             nan     0.0100    0.0001
   340        0.8990             nan     0.0100    0.0001
   360        0.8930             nan     0.0100    0.0001
   380        0.8881             nan     0.0100    0.0000
   400        0.8828             nan     0.0100    0.0001
   420        0.8773             nan     0.0100    0.0001
   440        0.8726             nan     0.0100    0.0000
   460        0.8680             nan     0.0100    0.0000
   480        0.8639             nan     0.0100    0.0001
   500        0.8600             nan     0.0100   -0.0000
   520        0.8565             nan     0.0100   -0.0001
   540        0.8527             nan     0.0100   -0.0000
   560        0.8493             nan     0.0100   -0.0000
   580        0.8460             nan     0.0100    0.0000
   600        0.8431             nan     0.0100   -0.0000
   620        0.8403             nan     0.0100    0.0000
   640        0.8376             nan     0.0100   -0.0000
   660        0.8345             nan     0.0100    0.0000
   680        0.8317             nan     0.0100    0.0000
   700        0.8289             nan     0.0100    0.0000
   720        0.8263             nan     0.0100   -0.0000
   740        0.8238             nan     0.0100    0.0000
   760        0.8216             nan     0.0100    0.0000
   780        0.8195             nan     0.0100   -0.0000
   800        0.8172             nan     0.0100   -0.0000
   820        0.8155             nan     0.0100   -0.0001
   840        0.8135             nan     0.0100    0.0000
   860        0.8115             nan     0.0100   -0.0000
   880        0.8097             nan     0.0100   -0.0001
   900        0.8080             nan     0.0100   -0.0001
   920        0.8064             nan     0.0100   -0.0000
   940        0.8049             nan     0.0100    0.0000
   960        0.8032             nan     0.0100   -0.0000
   980        0.8017             nan     0.0100   -0.0000
  1000        0.8002             nan     0.0100   -0.0000
  1020        0.7987             nan     0.0100   -0.0001
  1040        0.7971             nan     0.0100   -0.0000
  1060        0.7956             nan     0.0100   -0.0000
  1080        0.7943             nan     0.0100   -0.0000
  1100        0.7928             nan     0.0100    0.0000

- Fold03.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0036
     2        1.3185             nan     0.0100    0.0033
     3        1.3111             nan     0.0100    0.0034
     4        1.3041             nan     0.0100    0.0034
     5        1.2974             nan     0.0100    0.0033
     6        1.2910             nan     0.0100    0.0033
     7        1.2841             nan     0.0100    0.0032
     8        1.2779             nan     0.0100    0.0033
     9        1.2719             nan     0.0100    0.0029
    10        1.2656             nan     0.0100    0.0032
    20        1.2100             nan     0.0100    0.0025
    40        1.1234             nan     0.0100    0.0018
    60        1.0582             nan     0.0100    0.0012
    80        1.0108             nan     0.0100    0.0009
   100        0.9728             nan     0.0100    0.0007
   120        0.9454             nan     0.0100    0.0005
   140        0.9226             nan     0.0100    0.0004
   160        0.9043             nan     0.0100    0.0004
   180        0.8885             nan     0.0100    0.0003
   200        0.8751             nan     0.0100    0.0003
   220        0.8643             nan     0.0100    0.0001
   240        0.8545             nan     0.0100    0.0000
   260        0.8441             nan     0.0100   -0.0000
   280        0.8363             nan     0.0100   -0.0000
   300        0.8281             nan     0.0100    0.0002
   320        0.8209             nan     0.0100   -0.0000
   340        0.8144             nan     0.0100    0.0001
   360        0.8084             nan     0.0100    0.0001
   380        0.8030             nan     0.0100   -0.0000
   400        0.7973             nan     0.0100   -0.0001
   420        0.7926             nan     0.0100   -0.0001
   440        0.7882             nan     0.0100   -0.0001
   460        0.7837             nan     0.0100   -0.0001
   480        0.7800             nan     0.0100   -0.0001
   500        0.7760             nan     0.0100   -0.0001
   520        0.7721             nan     0.0100   -0.0000
   540        0.7689             nan     0.0100   -0.0000
   560        0.7655             nan     0.0100   -0.0000
   580        0.7623             nan     0.0100   -0.0001
   600        0.7594             nan     0.0100   -0.0000
   620        0.7561             nan     0.0100    0.0000
   640        0.7530             nan     0.0100   -0.0000
   660        0.7505             nan     0.0100   -0.0000
   680        0.7482             nan     0.0100    0.0000
   700        0.7459             nan     0.0100   -0.0002
   720        0.7433             nan     0.0100   -0.0001
   740        0.7404             nan     0.0100   -0.0001
   760        0.7383             nan     0.0100   -0.0001
   780        0.7358             nan     0.0100   -0.0000
   800        0.7328             nan     0.0100   -0.0001
   820        0.7307             nan     0.0100   -0.0001
   840        0.7283             nan     0.0100   -0.0002
   860        0.7260             nan     0.0100    0.0000
   880        0.7236             nan     0.0100   -0.0000
   900        0.7212             nan     0.0100   -0.0001
   920        0.7187             nan     0.0100   -0.0000
   940        0.7163             nan     0.0100   -0.0000
   960        0.7142             nan     0.0100   -0.0001
   980        0.7125             nan     0.0100   -0.0000
  1000        0.7104             nan     0.0100   -0.0001
  1020        0.7086             nan     0.0100   -0.0001
  1040        0.7064             nan     0.0100   -0.0000
  1060        0.7043             nan     0.0100   -0.0000
  1080        0.7024             nan     0.0100   -0.0001
  1100        0.7001             nan     0.0100   -0.0000

- Fold03.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0040
     2        1.3163             nan     0.0100    0.0041
     3        1.3080             nan     0.0100    0.0039
     4        1.3005             nan     0.0100    0.0036
     5        1.2936             nan     0.0100    0.0033
     6        1.2861             nan     0.0100    0.0035
     7        1.2789             nan     0.0100    0.0035
     8        1.2721             nan     0.0100    0.0032
     9        1.2651             nan     0.0100    0.0033
    10        1.2585             nan     0.0100    0.0033
    20        1.1981             nan     0.0100    0.0029
    40        1.1036             nan     0.0100    0.0020
    60        1.0329             nan     0.0100    0.0016
    80        0.9813             nan     0.0100    0.0011
   100        0.9412             nan     0.0100    0.0007
   120        0.9096             nan     0.0100    0.0005
   140        0.8865             nan     0.0100    0.0002
   160        0.8671             nan     0.0100    0.0001
   180        0.8498             nan     0.0100    0.0003
   200        0.8363             nan     0.0100    0.0002
   220        0.8241             nan     0.0100   -0.0001
   240        0.8131             nan     0.0100    0.0000
   260        0.8034             nan     0.0100   -0.0000
   280        0.7946             nan     0.0100    0.0001
   300        0.7871             nan     0.0100   -0.0001
   320        0.7798             nan     0.0100   -0.0001
   340        0.7734             nan     0.0100    0.0001
   360        0.7672             nan     0.0100    0.0000
   380        0.7606             nan     0.0100   -0.0001
   400        0.7553             nan     0.0100   -0.0002
   420        0.7495             nan     0.0100   -0.0000
   440        0.7447             nan     0.0100    0.0001
   460        0.7402             nan     0.0100   -0.0002
   480        0.7352             nan     0.0100   -0.0000
   500        0.7303             nan     0.0100   -0.0002
   520        0.7266             nan     0.0100   -0.0000
   540        0.7219             nan     0.0100   -0.0001
   560        0.7184             nan     0.0100   -0.0000
   580        0.7147             nan     0.0100   -0.0001
   600        0.7107             nan     0.0100   -0.0000
   620        0.7071             nan     0.0100   -0.0001
   640        0.7037             nan     0.0100   -0.0001
   660        0.7004             nan     0.0100   -0.0000
   680        0.6970             nan     0.0100   -0.0000
   700        0.6934             nan     0.0100   -0.0002
   720        0.6899             nan     0.0100   -0.0000
   740        0.6874             nan     0.0100   -0.0002
   760        0.6839             nan     0.0100   -0.0002
   780        0.6808             nan     0.0100   -0.0001
   800        0.6773             nan     0.0100   -0.0001
   820        0.6748             nan     0.0100   -0.0001
   840        0.6720             nan     0.0100   -0.0001
   860        0.6686             nan     0.0100   -0.0001
   880        0.6658             nan     0.0100   -0.0001
   900        0.6629             nan     0.0100   -0.0001
   920        0.6603             nan     0.0100    0.0000
   940        0.6577             nan     0.0100   -0.0001
   960        0.6553             nan     0.0100   -0.0002
   980        0.6526             nan     0.0100   -0.0000
  1000        0.6495             nan     0.0100   -0.0001
  1020        0.6470             nan     0.0100   -0.0001
  1040        0.6448             nan     0.0100   -0.0001
  1060        0.6422             nan     0.0100   -0.0002
  1080        0.6396             nan     0.0100    0.0000
  1100        0.6373             nan     0.0100   -0.0002

- Fold03.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2679             nan     0.1000    0.0253
     2        1.2214             nan     0.1000    0.0214
     3        1.1860             nan     0.1000    0.0177
     4        1.1562             nan     0.1000    0.0141
     5        1.1279             nan     0.1000    0.0117
     6        1.1079             nan     0.1000    0.0088
     7        1.0883             nan     0.1000    0.0085
     8        1.0718             nan     0.1000    0.0064
     9        1.0558             nan     0.1000    0.0076
    10        1.0408             nan     0.1000    0.0061
    20        0.9513             nan     0.1000    0.0018
    40        0.8760             nan     0.1000    0.0005
    60        0.8386             nan     0.1000   -0.0003
    80        0.8166             nan     0.1000    0.0000
   100        0.7994             nan     0.1000   -0.0004
   120        0.7869             nan     0.1000   -0.0014
   140        0.7756             nan     0.1000   -0.0006
   160        0.7662             nan     0.1000   -0.0003
   180        0.7580             nan     0.1000   -0.0011
   200        0.7529             nan     0.1000   -0.0008
   220        0.7467             nan     0.1000   -0.0012
   240        0.7416             nan     0.1000   -0.0010
   260        0.7348             nan     0.1000   -0.0003
   280        0.7306             nan     0.1000   -0.0004
   300        0.7271             nan     0.1000   -0.0005
   320        0.7220             nan     0.1000   -0.0009
   340        0.7168             nan     0.1000   -0.0009
   360        0.7123             nan     0.1000   -0.0006
   380        0.7083             nan     0.1000   -0.0007
   400        0.7064             nan     0.1000   -0.0007
   420        0.7024             nan     0.1000   -0.0002
   440        0.6993             nan     0.1000   -0.0005
   460        0.6964             nan     0.1000   -0.0015
   480        0.6928             nan     0.1000   -0.0010
   500        0.6911             nan     0.1000   -0.0001
   520        0.6884             nan     0.1000   -0.0005
   540        0.6860             nan     0.1000   -0.0010
   560        0.6843             nan     0.1000   -0.0009
   580        0.6827             nan     0.1000   -0.0011
   600        0.6814             nan     0.1000   -0.0009
   620        0.6794             nan     0.1000   -0.0003
   640        0.6774             nan     0.1000   -0.0010
   660        0.6749             nan     0.1000   -0.0008
   680        0.6722             nan     0.1000   -0.0007
   700        0.6702             nan     0.1000   -0.0004
   720        0.6691             nan     0.1000   -0.0005
   740        0.6665             nan     0.1000   -0.0014
   760        0.6645             nan     0.1000   -0.0013
   780        0.6631             nan     0.1000   -0.0012
   800        0.6612             nan     0.1000   -0.0004
   820        0.6593             nan     0.1000   -0.0005
   840        0.6577             nan     0.1000   -0.0014
   860        0.6561             nan     0.1000   -0.0005
   880        0.6555             nan     0.1000   -0.0012
   900        0.6546             nan     0.1000   -0.0006
   920        0.6524             nan     0.1000   -0.0008
   940        0.6501             nan     0.1000   -0.0008
   960        0.6479             nan     0.1000   -0.0005
   980        0.6461             nan     0.1000   -0.0007
  1000        0.6458             nan     0.1000   -0.0010
  1020        0.6447             nan     0.1000   -0.0005
  1040        0.6433             nan     0.1000   -0.0007
  1060        0.6416             nan     0.1000   -0.0006
  1080        0.6395             nan     0.1000   -0.0009
  1100        0.6383             nan     0.1000   -0.0007

- Fold03.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2657             nan     0.1000    0.0334
     2        1.2089             nan     0.1000    0.0261
     3        1.1568             nan     0.1000    0.0248
     4        1.1152             nan     0.1000    0.0200
     5        1.0793             nan     0.1000    0.0160
     6        1.0506             nan     0.1000    0.0134
     7        1.0244             nan     0.1000    0.0117
     8        1.0018             nan     0.1000    0.0099
     9        0.9843             nan     0.1000    0.0083
    10        0.9665             nan     0.1000    0.0075
    20        0.8720             nan     0.1000    0.0022
    40        0.8014             nan     0.1000   -0.0006
    60        0.7625             nan     0.1000   -0.0008
    80        0.7343             nan     0.1000   -0.0008
   100        0.7145             nan     0.1000   -0.0006
   120        0.6982             nan     0.1000   -0.0007
   140        0.6807             nan     0.1000   -0.0007
   160        0.6644             nan     0.1000   -0.0022
   180        0.6499             nan     0.1000   -0.0007
   200        0.6322             nan     0.1000   -0.0006
   220        0.6211             nan     0.1000   -0.0012
   240        0.6119             nan     0.1000   -0.0011
   260        0.6025             nan     0.1000   -0.0022
   280        0.5923             nan     0.1000   -0.0007
   300        0.5824             nan     0.1000   -0.0010
   320        0.5731             nan     0.1000   -0.0012
   340        0.5637             nan     0.1000   -0.0010
   360        0.5576             nan     0.1000   -0.0010
   380        0.5514             nan     0.1000   -0.0016
   400        0.5428             nan     0.1000   -0.0016
   420        0.5388             nan     0.1000   -0.0006
   440        0.5338             nan     0.1000   -0.0014
   460        0.5253             nan     0.1000   -0.0003
   480        0.5200             nan     0.1000   -0.0009
   500        0.5127             nan     0.1000   -0.0012
   520        0.5078             nan     0.1000   -0.0009
   540        0.5016             nan     0.1000   -0.0019
   560        0.4968             nan     0.1000   -0.0009
   580        0.4906             nan     0.1000   -0.0002
   600        0.4843             nan     0.1000   -0.0008
   620        0.4781             nan     0.1000   -0.0007
   640        0.4727             nan     0.1000   -0.0008
   660        0.4650             nan     0.1000   -0.0005
   680        0.4607             nan     0.1000   -0.0007
   700        0.4575             nan     0.1000   -0.0008
   720        0.4524             nan     0.1000   -0.0004
   740        0.4486             nan     0.1000   -0.0007
   760        0.4445             nan     0.1000   -0.0009
   780        0.4393             nan     0.1000   -0.0005
   800        0.4334             nan     0.1000   -0.0011
   820        0.4294             nan     0.1000   -0.0003
   840        0.4272             nan     0.1000   -0.0015
   860        0.4212             nan     0.1000   -0.0005
   880        0.4166             nan     0.1000   -0.0001
   900        0.4129             nan     0.1000   -0.0016
   920        0.4097             nan     0.1000   -0.0001
   940        0.4061             nan     0.1000   -0.0007
   960        0.4023             nan     0.1000   -0.0004
   980        0.3992             nan     0.1000   -0.0005
  1000        0.3954             nan     0.1000   -0.0014
  1020        0.3922             nan     0.1000   -0.0007
  1040        0.3899             nan     0.1000   -0.0003
  1060        0.3869             nan     0.1000   -0.0008
  1080        0.3850             nan     0.1000   -0.0009
  1100        0.3827             nan     0.1000   -0.0005

- Fold03.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2545             nan     0.1000    0.0374
     2        1.1936             nan     0.1000    0.0287
     3        1.1410             nan     0.1000    0.0267
     4        1.0957             nan     0.1000    0.0220
     5        1.0571             nan     0.1000    0.0172
     6        1.0232             nan     0.1000    0.0151
     7        0.9978             nan     0.1000    0.0120
     8        0.9749             nan     0.1000    0.0103
     9        0.9541             nan     0.1000    0.0091
    10        0.9386             nan     0.1000    0.0057
    20        0.8438             nan     0.1000    0.0014
    40        0.7681             nan     0.1000   -0.0003
    60        0.7254             nan     0.1000   -0.0015
    80        0.6847             nan     0.1000   -0.0008
   100        0.6630             nan     0.1000   -0.0017
   120        0.6363             nan     0.1000   -0.0008
   140        0.6136             nan     0.1000   -0.0016
   160        0.5951             nan     0.1000   -0.0007
   180        0.5754             nan     0.1000   -0.0004
   200        0.5574             nan     0.1000   -0.0013
   220        0.5428             nan     0.1000   -0.0004
   240        0.5274             nan     0.1000   -0.0010
   260        0.5095             nan     0.1000   -0.0012
   280        0.4995             nan     0.1000   -0.0006
   300        0.4880             nan     0.1000   -0.0011
   320        0.4759             nan     0.1000   -0.0020
   340        0.4652             nan     0.1000   -0.0014
   360        0.4532             nan     0.1000   -0.0005
   380        0.4425             nan     0.1000   -0.0018
   400        0.4342             nan     0.1000   -0.0012
   420        0.4249             nan     0.1000   -0.0010
   440        0.4153             nan     0.1000   -0.0013
   460        0.4107             nan     0.1000   -0.0019
   480        0.4052             nan     0.1000   -0.0010
   500        0.3979             nan     0.1000   -0.0011
   520        0.3904             nan     0.1000   -0.0008
   540        0.3842             nan     0.1000   -0.0012
   560        0.3768             nan     0.1000   -0.0011
   580        0.3699             nan     0.1000   -0.0011
   600        0.3638             nan     0.1000   -0.0017
   620        0.3582             nan     0.1000   -0.0009
   640        0.3523             nan     0.1000   -0.0014
   660        0.3462             nan     0.1000   -0.0001
   680        0.3420             nan     0.1000   -0.0011
   700        0.3359             nan     0.1000   -0.0004
   720        0.3316             nan     0.1000   -0.0008
   740        0.3266             nan     0.1000   -0.0010
   760        0.3214             nan     0.1000   -0.0005
   780        0.3163             nan     0.1000   -0.0018
   800        0.3110             nan     0.1000   -0.0005
   820        0.3068             nan     0.1000   -0.0005
   840        0.3016             nan     0.1000   -0.0020
   860        0.2972             nan     0.1000   -0.0010
   880        0.2930             nan     0.1000   -0.0009
   900        0.2889             nan     0.1000   -0.0005
   920        0.2852             nan     0.1000   -0.0009
   940        0.2815             nan     0.1000   -0.0017
   960        0.2780             nan     0.1000   -0.0008
   980        0.2745             nan     0.1000   -0.0004
  1000        0.2707             nan     0.1000   -0.0007
  1020        0.2667             nan     0.1000   -0.0008
  1040        0.2643             nan     0.1000   -0.0018
  1060        0.2618             nan     0.1000   -0.0007
  1080        0.2579             nan     0.1000   -0.0006
  1100        0.2550             nan     0.1000   -0.0004

- Fold03.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3252             nan     0.0100    0.0031
     2        1.3195             nan     0.0100    0.0031
     3        1.3132             nan     0.0100    0.0030
     4        1.3075             nan     0.0100    0.0030
     5        1.3019             nan     0.0100    0.0029
     6        1.2959             nan     0.0100    0.0028
     7        1.2902             nan     0.0100    0.0028
     8        1.2844             nan     0.0100    0.0027
     9        1.2795             nan     0.0100    0.0027
    10        1.2742             nan     0.0100    0.0025
    20        1.2254             nan     0.0100    0.0022
    40        1.1533             nan     0.0100    0.0015
    60        1.1019             nan     0.0100    0.0011
    80        1.0638             nan     0.0100    0.0007
   100        1.0347             nan     0.0100    0.0006
   120        1.0104             nan     0.0100    0.0005
   140        0.9891             nan     0.0100    0.0004
   160        0.9716             nan     0.0100    0.0004
   180        0.9567             nan     0.0100    0.0003
   200        0.9429             nan     0.0100    0.0002
   220        0.9311             nan     0.0100    0.0002
   240        0.9199             nan     0.0100    0.0001
   260        0.9102             nan     0.0100    0.0002
   280        0.9025             nan     0.0100    0.0000
   300        0.8948             nan     0.0100    0.0001
   320        0.8882             nan     0.0100    0.0001
   340        0.8817             nan     0.0100    0.0001
   360        0.8758             nan     0.0100    0.0001
   380        0.8704             nan     0.0100    0.0001
   400        0.8654             nan     0.0100    0.0000
   420        0.8606             nan     0.0100    0.0001
   440        0.8559             nan     0.0100    0.0001
   460        0.8518             nan     0.0100    0.0000
   480        0.8477             nan     0.0100   -0.0000
   500        0.8439             nan     0.0100    0.0000
   520        0.8406             nan     0.0100   -0.0001
   540        0.8370             nan     0.0100    0.0000
   560        0.8335             nan     0.0100    0.0000
   580        0.8303             nan     0.0100    0.0000
   600        0.8271             nan     0.0100    0.0000
   620        0.8244             nan     0.0100    0.0000
   640        0.8216             nan     0.0100   -0.0000
   660        0.8192             nan     0.0100   -0.0000
   680        0.8165             nan     0.0100   -0.0001
   700        0.8140             nan     0.0100    0.0000
   720        0.8115             nan     0.0100   -0.0000
   740        0.8093             nan     0.0100   -0.0001
   760        0.8071             nan     0.0100   -0.0001
   780        0.8050             nan     0.0100   -0.0000
   800        0.8030             nan     0.0100   -0.0000
   820        0.8008             nan     0.0100   -0.0000
   840        0.7990             nan     0.0100   -0.0000
   860        0.7971             nan     0.0100   -0.0000
   880        0.7952             nan     0.0100   -0.0001
   900        0.7934             nan     0.0100    0.0000
   920        0.7918             nan     0.0100    0.0000
   940        0.7900             nan     0.0100   -0.0000
   960        0.7884             nan     0.0100   -0.0000
   980        0.7868             nan     0.0100   -0.0000
  1000        0.7851             nan     0.0100    0.0000
  1020        0.7837             nan     0.0100   -0.0001
  1040        0.7822             nan     0.0100   -0.0000
  1060        0.7809             nan     0.0100   -0.0000
  1080        0.7795             nan     0.0100   -0.0001
  1100        0.7785             nan     0.0100   -0.0001

- Fold04.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0039
     2        1.3163             nan     0.0100    0.0037
     3        1.3085             nan     0.0100    0.0040
     4        1.3013             nan     0.0100    0.0035
     5        1.2942             nan     0.0100    0.0036
     6        1.2869             nan     0.0100    0.0035
     7        1.2801             nan     0.0100    0.0033
     8        1.2735             nan     0.0100    0.0033
     9        1.2674             nan     0.0100    0.0036
    10        1.2606             nan     0.0100    0.0033
    20        1.2010             nan     0.0100    0.0025
    40        1.1097             nan     0.0100    0.0019
    60        1.0438             nan     0.0100    0.0013
    80        0.9936             nan     0.0100    0.0008
   100        0.9569             nan     0.0100    0.0008
   120        0.9280             nan     0.0100    0.0007
   140        0.9043             nan     0.0100    0.0005
   160        0.8866             nan     0.0100    0.0002
   180        0.8720             nan     0.0100    0.0003
   200        0.8590             nan     0.0100    0.0001
   220        0.8478             nan     0.0100    0.0001
   240        0.8377             nan     0.0100    0.0001
   260        0.8284             nan     0.0100    0.0002
   280        0.8205             nan     0.0100   -0.0000
   300        0.8120             nan     0.0100    0.0001
   320        0.8055             nan     0.0100    0.0001
   340        0.7997             nan     0.0100   -0.0000
   360        0.7941             nan     0.0100   -0.0001
   380        0.7886             nan     0.0100   -0.0000
   400        0.7839             nan     0.0100    0.0000
   420        0.7791             nan     0.0100   -0.0001
   440        0.7746             nan     0.0100   -0.0000
   460        0.7700             nan     0.0100    0.0001
   480        0.7662             nan     0.0100   -0.0001
   500        0.7624             nan     0.0100   -0.0001
   520        0.7588             nan     0.0100   -0.0001
   540        0.7553             nan     0.0100    0.0000
   560        0.7517             nan     0.0100   -0.0001
   580        0.7485             nan     0.0100   -0.0001
   600        0.7454             nan     0.0100   -0.0001
   620        0.7423             nan     0.0100   -0.0001
   640        0.7395             nan     0.0100   -0.0001
   660        0.7368             nan     0.0100   -0.0000
   680        0.7342             nan     0.0100   -0.0001
   700        0.7319             nan     0.0100   -0.0002
   720        0.7296             nan     0.0100   -0.0001
   740        0.7266             nan     0.0100   -0.0001
   760        0.7243             nan     0.0100   -0.0001
   780        0.7222             nan     0.0100   -0.0001
   800        0.7201             nan     0.0100   -0.0000
   820        0.7179             nan     0.0100   -0.0001
   840        0.7156             nan     0.0100   -0.0000
   860        0.7135             nan     0.0100   -0.0002
   880        0.7112             nan     0.0100   -0.0000
   900        0.7092             nan     0.0100   -0.0001
   920        0.7069             nan     0.0100   -0.0002
   940        0.7052             nan     0.0100   -0.0001
   960        0.7029             nan     0.0100   -0.0000
   980        0.7011             nan     0.0100   -0.0002
  1000        0.6995             nan     0.0100   -0.0001
  1020        0.6975             nan     0.0100   -0.0001
  1040        0.6956             nan     0.0100   -0.0001
  1060        0.6938             nan     0.0100   -0.0001
  1080        0.6917             nan     0.0100   -0.0001
  1100        0.6897             nan     0.0100   -0.0001

- Fold04.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3233             nan     0.0100    0.0043
     2        1.3148             nan     0.0100    0.0040
     3        1.3068             nan     0.0100    0.0040
     4        1.2989             nan     0.0100    0.0041
     5        1.2913             nan     0.0100    0.0036
     6        1.2838             nan     0.0100    0.0037
     7        1.2758             nan     0.0100    0.0036
     8        1.2687             nan     0.0100    0.0036
     9        1.2618             nan     0.0100    0.0035
    10        1.2548             nan     0.0100    0.0034
    20        1.1919             nan     0.0100    0.0028
    40        1.0919             nan     0.0100    0.0020
    60        1.0198             nan     0.0100    0.0013
    80        0.9651             nan     0.0100    0.0010
   100        0.9237             nan     0.0100    0.0008
   120        0.8931             nan     0.0100    0.0005
   140        0.8695             nan     0.0100    0.0003
   160        0.8513             nan     0.0100    0.0003
   180        0.8341             nan     0.0100    0.0002
   200        0.8196             nan     0.0100   -0.0001
   220        0.8080             nan     0.0100    0.0002
   240        0.7976             nan     0.0100    0.0001
   260        0.7879             nan     0.0100    0.0000
   280        0.7796             nan     0.0100   -0.0000
   300        0.7719             nan     0.0100   -0.0000
   320        0.7653             nan     0.0100    0.0000
   340        0.7585             nan     0.0100   -0.0001
   360        0.7532             nan     0.0100   -0.0001
   380        0.7472             nan     0.0100   -0.0000
   400        0.7416             nan     0.0100    0.0000
   420        0.7359             nan     0.0100   -0.0000
   440        0.7307             nan     0.0100   -0.0000
   460        0.7265             nan     0.0100   -0.0000
   480        0.7222             nan     0.0100   -0.0000
   500        0.7180             nan     0.0100   -0.0000
   520        0.7139             nan     0.0100   -0.0000
   540        0.7102             nan     0.0100   -0.0000
   560        0.7063             nan     0.0100    0.0000
   580        0.7030             nan     0.0100   -0.0003
   600        0.6993             nan     0.0100   -0.0001
   620        0.6951             nan     0.0100   -0.0001
   640        0.6919             nan     0.0100   -0.0001
   660        0.6891             nan     0.0100   -0.0001
   680        0.6858             nan     0.0100   -0.0000
   700        0.6825             nan     0.0100   -0.0002
   720        0.6792             nan     0.0100   -0.0001
   740        0.6762             nan     0.0100   -0.0001
   760        0.6732             nan     0.0100   -0.0001
   780        0.6704             nan     0.0100   -0.0001
   800        0.6676             nan     0.0100   -0.0001
   820        0.6647             nan     0.0100   -0.0001
   840        0.6619             nan     0.0100   -0.0001
   860        0.6587             nan     0.0100   -0.0001
   880        0.6558             nan     0.0100   -0.0000
   900        0.6530             nan     0.0100   -0.0001
   920        0.6506             nan     0.0100   -0.0001
   940        0.6480             nan     0.0100   -0.0000
   960        0.6456             nan     0.0100   -0.0001
   980        0.6430             nan     0.0100   -0.0001
  1000        0.6408             nan     0.0100   -0.0001
  1020        0.6384             nan     0.0100   -0.0001
  1040        0.6365             nan     0.0100   -0.0002
  1060        0.6342             nan     0.0100   -0.0001
  1080        0.6324             nan     0.0100   -0.0002
  1100        0.6303             nan     0.0100   -0.0001

- Fold04.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2640             nan     0.1000    0.0289
     2        1.2160             nan     0.1000    0.0233
     3        1.1763             nan     0.1000    0.0187
     4        1.1439             nan     0.1000    0.0161
     5        1.1147             nan     0.1000    0.0131
     6        1.0936             nan     0.1000    0.0109
     7        1.0742             nan     0.1000    0.0085
     8        1.0586             nan     0.1000    0.0078
     9        1.0417             nan     0.1000    0.0077
    10        1.0288             nan     0.1000    0.0058
    20        0.9371             nan     0.1000    0.0022
    40        0.8655             nan     0.1000   -0.0000
    60        0.8273             nan     0.1000    0.0000
    80        0.8013             nan     0.1000   -0.0009
   100        0.7859             nan     0.1000   -0.0020
   120        0.7729             nan     0.1000   -0.0001
   140        0.7637             nan     0.1000   -0.0007
   160        0.7531             nan     0.1000   -0.0003
   180        0.7466             nan     0.1000   -0.0013
   200        0.7404             nan     0.1000   -0.0006
   220        0.7353             nan     0.1000   -0.0010
   240        0.7301             nan     0.1000   -0.0007
   260        0.7251             nan     0.1000   -0.0005
   280        0.7204             nan     0.1000   -0.0005
   300        0.7157             nan     0.1000   -0.0003
   320        0.7126             nan     0.1000   -0.0008
   340        0.7093             nan     0.1000   -0.0004
   360        0.7054             nan     0.1000   -0.0010
   380        0.7019             nan     0.1000   -0.0006
   400        0.6991             nan     0.1000   -0.0018
   420        0.6965             nan     0.1000   -0.0023
   440        0.6931             nan     0.1000   -0.0006
   460        0.6904             nan     0.1000   -0.0010
   480        0.6882             nan     0.1000   -0.0005
   500        0.6867             nan     0.1000   -0.0002
   520        0.6842             nan     0.1000   -0.0007
   540        0.6842             nan     0.1000   -0.0013
   560        0.6819             nan     0.1000   -0.0006
   580        0.6795             nan     0.1000   -0.0006
   600        0.6783             nan     0.1000   -0.0006
   620        0.6760             nan     0.1000   -0.0006
   640        0.6746             nan     0.1000   -0.0011
   660        0.6732             nan     0.1000   -0.0004
   680        0.6717             nan     0.1000   -0.0006
   700        0.6698             nan     0.1000   -0.0011
   720        0.6672             nan     0.1000   -0.0007
   740        0.6655             nan     0.1000   -0.0022
   760        0.6642             nan     0.1000   -0.0008
   780        0.6630             nan     0.1000   -0.0008
   800        0.6602             nan     0.1000   -0.0002
   820        0.6584             nan     0.1000   -0.0005
   840        0.6561             nan     0.1000   -0.0010
   860        0.6540             nan     0.1000   -0.0004
   880        0.6525             nan     0.1000   -0.0013
   900        0.6512             nan     0.1000   -0.0010
   920        0.6498             nan     0.1000   -0.0014
   940        0.6476             nan     0.1000   -0.0006
   960        0.6461             nan     0.1000   -0.0004
   980        0.6461             nan     0.1000   -0.0006
  1000        0.6439             nan     0.1000   -0.0003
  1020        0.6427             nan     0.1000   -0.0013
  1040        0.6424             nan     0.1000   -0.0009
  1060        0.6409             nan     0.1000   -0.0005
  1080        0.6398             nan     0.1000   -0.0005
  1100        0.6390             nan     0.1000   -0.0006

- Fold04.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2562             nan     0.1000    0.0363
     2        1.1917             nan     0.1000    0.0297
     3        1.1430             nan     0.1000    0.0213
     4        1.1002             nan     0.1000    0.0207
     5        1.0655             nan     0.1000    0.0177
     6        1.0342             nan     0.1000    0.0158
     7        1.0061             nan     0.1000    0.0118
     8        0.9845             nan     0.1000    0.0090
     9        0.9669             nan     0.1000    0.0095
    10        0.9486             nan     0.1000    0.0085
    20        0.8594             nan     0.1000    0.0016
    40        0.7871             nan     0.1000   -0.0004
    60        0.7487             nan     0.1000   -0.0016
    80        0.7235             nan     0.1000   -0.0003
   100        0.6983             nan     0.1000    0.0001
   120        0.6827             nan     0.1000   -0.0014
   140        0.6649             nan     0.1000   -0.0003
   160        0.6532             nan     0.1000   -0.0004
   180        0.6390             nan     0.1000   -0.0017
   200        0.6278             nan     0.1000   -0.0003
   220        0.6154             nan     0.1000   -0.0006
   240        0.6062             nan     0.1000   -0.0009
   260        0.5972             nan     0.1000   -0.0005
   280        0.5884             nan     0.1000   -0.0008
   300        0.5779             nan     0.1000   -0.0018
   320        0.5680             nan     0.1000   -0.0005
   340        0.5616             nan     0.1000   -0.0004
   360        0.5532             nan     0.1000   -0.0014
   380        0.5464             nan     0.1000   -0.0005
   400        0.5405             nan     0.1000   -0.0009
   420        0.5308             nan     0.1000   -0.0011
   440        0.5255             nan     0.1000   -0.0008
   460        0.5200             nan     0.1000   -0.0010
   480        0.5131             nan     0.1000   -0.0006
   500        0.5072             nan     0.1000   -0.0014
   520        0.5024             nan     0.1000   -0.0008
   540        0.4970             nan     0.1000   -0.0016
   560        0.4919             nan     0.1000   -0.0015
   580        0.4867             nan     0.1000   -0.0011
   600        0.4817             nan     0.1000   -0.0006
   620        0.4743             nan     0.1000   -0.0009
   640        0.4700             nan     0.1000   -0.0011
   660        0.4654             nan     0.1000   -0.0011
   680        0.4609             nan     0.1000   -0.0008
   700        0.4557             nan     0.1000   -0.0014
   720        0.4502             nan     0.1000   -0.0015
   740        0.4456             nan     0.1000   -0.0005
   760        0.4417             nan     0.1000   -0.0013
   780        0.4394             nan     0.1000   -0.0014
   800        0.4351             nan     0.1000   -0.0015
   820        0.4315             nan     0.1000   -0.0005
   840        0.4263             nan     0.1000   -0.0006
   860        0.4221             nan     0.1000   -0.0011
   880        0.4200             nan     0.1000   -0.0009
   900        0.4163             nan     0.1000   -0.0010
   920        0.4119             nan     0.1000   -0.0014
   940        0.4085             nan     0.1000   -0.0008
   960        0.4047             nan     0.1000   -0.0006
   980        0.4008             nan     0.1000   -0.0012
  1000        0.3972             nan     0.1000   -0.0009
  1020        0.3930             nan     0.1000   -0.0007
  1040        0.3901             nan     0.1000   -0.0008
  1060        0.3869             nan     0.1000   -0.0015
  1080        0.3831             nan     0.1000   -0.0010
  1100        0.3811             nan     0.1000   -0.0017

- Fold04.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2468             nan     0.1000    0.0400
     2        1.1837             nan     0.1000    0.0310
     3        1.1310             nan     0.1000    0.0257
     4        1.0863             nan     0.1000    0.0212
     5        1.0492             nan     0.1000    0.0157
     6        1.0170             nan     0.1000    0.0148
     7        0.9906             nan     0.1000    0.0099
     8        0.9643             nan     0.1000    0.0112
     9        0.9400             nan     0.1000    0.0113
    10        0.9196             nan     0.1000    0.0075
    20        0.8254             nan     0.1000   -0.0003
    40        0.7445             nan     0.1000   -0.0018
    60        0.7010             nan     0.1000   -0.0022
    80        0.6707             nan     0.1000   -0.0007
   100        0.6423             nan     0.1000   -0.0011
   120        0.6178             nan     0.1000   -0.0011
   140        0.5985             nan     0.1000   -0.0013
   160        0.5793             nan     0.1000   -0.0012
   180        0.5583             nan     0.1000   -0.0014
   200        0.5414             nan     0.1000   -0.0011
   220        0.5248             nan     0.1000   -0.0002
   240        0.5109             nan     0.1000   -0.0007
   260        0.5009             nan     0.1000   -0.0014
   280        0.4882             nan     0.1000   -0.0008
   300        0.4772             nan     0.1000    0.0001
   320        0.4661             nan     0.1000   -0.0009
   340        0.4578             nan     0.1000   -0.0012
   360        0.4456             nan     0.1000   -0.0006
   380        0.4406             nan     0.1000   -0.0009
   400        0.4340             nan     0.1000   -0.0018
   420        0.4225             nan     0.1000   -0.0010
   440        0.4140             nan     0.1000   -0.0011
   460        0.4067             nan     0.1000   -0.0013
   480        0.3970             nan     0.1000   -0.0011
   500        0.3891             nan     0.1000   -0.0010
   520        0.3836             nan     0.1000   -0.0009
   540        0.3770             nan     0.1000   -0.0010
   560        0.3707             nan     0.1000   -0.0026
   580        0.3644             nan     0.1000   -0.0014
   600        0.3584             nan     0.1000   -0.0009
   620        0.3522             nan     0.1000   -0.0008
   640        0.3465             nan     0.1000   -0.0007
   660        0.3398             nan     0.1000   -0.0013
   680        0.3347             nan     0.1000   -0.0008
   700        0.3287             nan     0.1000   -0.0010
   720        0.3246             nan     0.1000   -0.0015
   740        0.3195             nan     0.1000   -0.0009
   760        0.3157             nan     0.1000   -0.0005
   780        0.3110             nan     0.1000   -0.0007
   800        0.3070             nan     0.1000   -0.0010
   820        0.3007             nan     0.1000   -0.0007
   840        0.2960             nan     0.1000   -0.0004
   860        0.2931             nan     0.1000   -0.0006
   880        0.2891             nan     0.1000   -0.0011
   900        0.2852             nan     0.1000   -0.0010
   920        0.2808             nan     0.1000   -0.0020
   940        0.2779             nan     0.1000   -0.0012
   960        0.2738             nan     0.1000   -0.0007
   980        0.2714             nan     0.1000   -0.0007
  1000        0.2681             nan     0.1000   -0.0007
  1020        0.2657             nan     0.1000   -0.0007
  1040        0.2613             nan     0.1000   -0.0009
  1060        0.2582             nan     0.1000   -0.0008
  1080        0.2546             nan     0.1000   -0.0004
  1100        0.2520             nan     0.1000   -0.0009

- Fold04.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3261             nan     0.0100    0.0029
     2        1.3205             nan     0.0100    0.0028
     3        1.3146             nan     0.0100    0.0029
     4        1.3098             nan     0.0100    0.0027
     5        1.3049             nan     0.0100    0.0027
     6        1.2997             nan     0.0100    0.0027
     7        1.2943             nan     0.0100    0.0026
     8        1.2890             nan     0.0100    0.0026
     9        1.2834             nan     0.0100    0.0025
    10        1.2785             nan     0.0100    0.0025
    20        1.2341             nan     0.0100    0.0020
    40        1.1633             nan     0.0100    0.0014
    60        1.1157             nan     0.0100    0.0010
    80        1.0793             nan     0.0100    0.0008
   100        1.0501             nan     0.0100    0.0006
   120        1.0253             nan     0.0100    0.0005
   140        1.0054             nan     0.0100    0.0004
   160        0.9887             nan     0.0100    0.0002
   180        0.9742             nan     0.0100    0.0001
   200        0.9614             nan     0.0100    0.0002
   220        0.9498             nan     0.0100    0.0002
   240        0.9397             nan     0.0100    0.0001
   260        0.9307             nan     0.0100    0.0002
   280        0.9221             nan     0.0100    0.0001
   300        0.9154             nan     0.0100    0.0001
   320        0.9088             nan     0.0100    0.0001
   340        0.9019             nan     0.0100    0.0001
   360        0.8965             nan     0.0100    0.0000
   380        0.8912             nan     0.0100   -0.0000
   400        0.8866             nan     0.0100    0.0000
   420        0.8818             nan     0.0100    0.0000
   440        0.8769             nan     0.0100   -0.0000
   460        0.8727             nan     0.0100   -0.0000
   480        0.8691             nan     0.0100    0.0000
   500        0.8651             nan     0.0100   -0.0000
   520        0.8615             nan     0.0100    0.0000
   540        0.8583             nan     0.0100    0.0000
   560        0.8551             nan     0.0100    0.0000
   580        0.8519             nan     0.0100   -0.0000
   600        0.8488             nan     0.0100   -0.0001
   620        0.8458             nan     0.0100   -0.0000
   640        0.8431             nan     0.0100   -0.0000
   660        0.8403             nan     0.0100   -0.0000
   680        0.8379             nan     0.0100    0.0000
   700        0.8356             nan     0.0100   -0.0001
   720        0.8329             nan     0.0100   -0.0001
   740        0.8304             nan     0.0100   -0.0000
   760        0.8281             nan     0.0100    0.0000
   780        0.8259             nan     0.0100   -0.0001
   800        0.8238             nan     0.0100   -0.0000
   820        0.8216             nan     0.0100   -0.0001
   840        0.8197             nan     0.0100   -0.0000
   860        0.8178             nan     0.0100    0.0000
   880        0.8158             nan     0.0100    0.0000
   900        0.8143             nan     0.0100   -0.0001
   920        0.8127             nan     0.0100   -0.0000
   940        0.8111             nan     0.0100   -0.0001
   960        0.8092             nan     0.0100   -0.0001
   980        0.8077             nan     0.0100   -0.0001
  1000        0.8060             nan     0.0100   -0.0000
  1020        0.8046             nan     0.0100   -0.0001
  1040        0.8034             nan     0.0100   -0.0001
  1060        0.8020             nan     0.0100   -0.0001
  1080        0.8008             nan     0.0100   -0.0001
  1100        0.7993             nan     0.0100   -0.0001

- Fold05.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3247             nan     0.0100    0.0037
     2        1.3176             nan     0.0100    0.0034
     3        1.3104             nan     0.0100    0.0037
     4        1.3032             nan     0.0100    0.0034
     5        1.2966             nan     0.0100    0.0031
     6        1.2906             nan     0.0100    0.0033
     7        1.2839             nan     0.0100    0.0030
     8        1.2773             nan     0.0100    0.0028
     9        1.2704             nan     0.0100    0.0031
    10        1.2644             nan     0.0100    0.0032
    20        1.2089             nan     0.0100    0.0022
    40        1.1211             nan     0.0100    0.0016
    60        1.0571             nan     0.0100    0.0012
    80        1.0099             nan     0.0100    0.0010
   100        0.9753             nan     0.0100    0.0005
   120        0.9473             nan     0.0100    0.0006
   140        0.9246             nan     0.0100    0.0002
   160        0.9071             nan     0.0100    0.0002
   180        0.8923             nan     0.0100    0.0002
   200        0.8791             nan     0.0100    0.0001
   220        0.8679             nan     0.0100    0.0002
   240        0.8581             nan     0.0100    0.0001
   260        0.8486             nan     0.0100    0.0001
   280        0.8411             nan     0.0100    0.0000
   300        0.8336             nan     0.0100    0.0001
   320        0.8260             nan     0.0100   -0.0000
   340        0.8194             nan     0.0100    0.0000
   360        0.8130             nan     0.0100    0.0001
   380        0.8071             nan     0.0100   -0.0000
   400        0.8022             nan     0.0100    0.0001
   420        0.7972             nan     0.0100    0.0000
   440        0.7917             nan     0.0100    0.0000
   460        0.7874             nan     0.0100    0.0000
   480        0.7834             nan     0.0100   -0.0001
   500        0.7793             nan     0.0100   -0.0001
   520        0.7753             nan     0.0100    0.0000
   540        0.7718             nan     0.0100   -0.0000
   560        0.7685             nan     0.0100   -0.0001
   580        0.7652             nan     0.0100   -0.0001
   600        0.7619             nan     0.0100   -0.0000
   620        0.7588             nan     0.0100   -0.0001
   640        0.7558             nan     0.0100   -0.0001
   660        0.7529             nan     0.0100   -0.0001
   680        0.7500             nan     0.0100   -0.0001
   700        0.7471             nan     0.0100   -0.0001
   720        0.7442             nan     0.0100    0.0000
   740        0.7417             nan     0.0100   -0.0001
   760        0.7391             nan     0.0100   -0.0001
   780        0.7365             nan     0.0100   -0.0000
   800        0.7341             nan     0.0100   -0.0001
   820        0.7317             nan     0.0100   -0.0000
   840        0.7291             nan     0.0100   -0.0001
   860        0.7271             nan     0.0100   -0.0002
   880        0.7251             nan     0.0100   -0.0000
   900        0.7231             nan     0.0100   -0.0000
   920        0.7209             nan     0.0100   -0.0001
   940        0.7189             nan     0.0100   -0.0001
   960        0.7164             nan     0.0100   -0.0000
   980        0.7145             nan     0.0100   -0.0000
  1000        0.7126             nan     0.0100   -0.0000
  1020        0.7105             nan     0.0100   -0.0000
  1040        0.7088             nan     0.0100   -0.0003
  1060        0.7070             nan     0.0100   -0.0001
  1080        0.7055             nan     0.0100   -0.0001
  1100        0.7035             nan     0.0100   -0.0000

- Fold05.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0040
     2        1.3167             nan     0.0100    0.0038
     3        1.3087             nan     0.0100    0.0039
     4        1.3013             nan     0.0100    0.0036
     5        1.2940             nan     0.0100    0.0036
     6        1.2867             nan     0.0100    0.0033
     7        1.2798             nan     0.0100    0.0037
     8        1.2732             nan     0.0100    0.0034
     9        1.2666             nan     0.0100    0.0032
    10        1.2596             nan     0.0100    0.0034
    20        1.1968             nan     0.0100    0.0028
    40        1.1006             nan     0.0100    0.0019
    60        1.0316             nan     0.0100    0.0015
    80        0.9786             nan     0.0100    0.0011
   100        0.9396             nan     0.0100    0.0007
   120        0.9098             nan     0.0100    0.0004
   140        0.8870             nan     0.0100    0.0003
   160        0.8672             nan     0.0100    0.0003
   180        0.8512             nan     0.0100    0.0002
   200        0.8376             nan     0.0100    0.0001
   220        0.8251             nan     0.0100    0.0001
   240        0.8146             nan     0.0100    0.0002
   260        0.8044             nan     0.0100    0.0001
   280        0.7958             nan     0.0100    0.0001
   300        0.7871             nan     0.0100   -0.0000
   320        0.7794             nan     0.0100    0.0001
   340        0.7718             nan     0.0100   -0.0000
   360        0.7658             nan     0.0100   -0.0000
   380        0.7597             nan     0.0100    0.0000
   400        0.7537             nan     0.0100    0.0000
   420        0.7486             nan     0.0100    0.0000
   440        0.7437             nan     0.0100   -0.0000
   460        0.7391             nan     0.0100   -0.0001
   480        0.7342             nan     0.0100   -0.0001
   500        0.7295             nan     0.0100   -0.0000
   520        0.7254             nan     0.0100   -0.0003
   540        0.7214             nan     0.0100    0.0000
   560        0.7179             nan     0.0100   -0.0000
   580        0.7144             nan     0.0100   -0.0001
   600        0.7110             nan     0.0100   -0.0001
   620        0.7074             nan     0.0100   -0.0001
   640        0.7041             nan     0.0100   -0.0001
   660        0.7008             nan     0.0100   -0.0001
   680        0.6968             nan     0.0100   -0.0001
   700        0.6937             nan     0.0100   -0.0000
   720        0.6902             nan     0.0100   -0.0002
   740        0.6866             nan     0.0100   -0.0001
   760        0.6837             nan     0.0100   -0.0001
   780        0.6806             nan     0.0100   -0.0001
   800        0.6775             nan     0.0100   -0.0001
   820        0.6745             nan     0.0100   -0.0001
   840        0.6716             nan     0.0100   -0.0001
   860        0.6692             nan     0.0100   -0.0001
   880        0.6666             nan     0.0100   -0.0001
   900        0.6636             nan     0.0100   -0.0001
   920        0.6606             nan     0.0100   -0.0002
   940        0.6580             nan     0.0100   -0.0001
   960        0.6557             nan     0.0100   -0.0003
   980        0.6531             nan     0.0100   -0.0001
  1000        0.6506             nan     0.0100   -0.0001
  1020        0.6478             nan     0.0100   -0.0001
  1040        0.6455             nan     0.0100   -0.0002
  1060        0.6432             nan     0.0100   -0.0001
  1080        0.6410             nan     0.0100   -0.0001
  1100        0.6380             nan     0.0100   -0.0001

- Fold05.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2706             nan     0.1000    0.0283
     2        1.2232             nan     0.1000    0.0225
     3        1.1832             nan     0.1000    0.0173
     4        1.1513             nan     0.1000    0.0146
     5        1.1267             nan     0.1000    0.0123
     6        1.1045             nan     0.1000    0.0093
     7        1.0883             nan     0.1000    0.0081
     8        1.0705             nan     0.1000    0.0077
     9        1.0562             nan     0.1000    0.0062
    10        1.0434             nan     0.1000    0.0068
    20        0.9574             nan     0.1000    0.0025
    40        0.8837             nan     0.1000    0.0003
    60        0.8478             nan     0.1000    0.0006
    80        0.8255             nan     0.1000   -0.0002
   100        0.8062             nan     0.1000    0.0000
   120        0.7943             nan     0.1000   -0.0010
   140        0.7818             nan     0.1000   -0.0003
   160        0.7731             nan     0.1000   -0.0003
   180        0.7648             nan     0.1000   -0.0007
   200        0.7578             nan     0.1000   -0.0006
   220        0.7518             nan     0.1000   -0.0013
   240        0.7475             nan     0.1000   -0.0004
   260        0.7424             nan     0.1000   -0.0007
   280        0.7377             nan     0.1000    0.0001
   300        0.7358             nan     0.1000    0.0000
   320        0.7327             nan     0.1000   -0.0010
   340        0.7309             nan     0.1000   -0.0002
   360        0.7277             nan     0.1000   -0.0008
   380        0.7235             nan     0.1000   -0.0008
   400        0.7209             nan     0.1000   -0.0008
   420        0.7200             nan     0.1000   -0.0008
   440        0.7156             nan     0.1000   -0.0009
   460        0.7112             nan     0.1000   -0.0012
   480        0.7080             nan     0.1000   -0.0003
   500        0.7055             nan     0.1000   -0.0007
   520        0.7042             nan     0.1000   -0.0007
   540        0.7017             nan     0.1000   -0.0008
   560        0.6983             nan     0.1000   -0.0008
   580        0.6956             nan     0.1000   -0.0006
   600        0.6941             nan     0.1000   -0.0005
   620        0.6927             nan     0.1000   -0.0009
   640        0.6907             nan     0.1000   -0.0006
   660        0.6891             nan     0.1000   -0.0008
   680        0.6867             nan     0.1000   -0.0011
   700        0.6846             nan     0.1000   -0.0006
   720        0.6830             nan     0.1000   -0.0011
   740        0.6815             nan     0.1000   -0.0006
   760        0.6794             nan     0.1000   -0.0017
   780        0.6777             nan     0.1000   -0.0008
   800        0.6769             nan     0.1000   -0.0008
   820        0.6749             nan     0.1000   -0.0006
   840        0.6731             nan     0.1000   -0.0008
   860        0.6722             nan     0.1000   -0.0011
   880        0.6705             nan     0.1000   -0.0009
   900        0.6690             nan     0.1000   -0.0002
   920        0.6673             nan     0.1000   -0.0007
   940        0.6664             nan     0.1000   -0.0007
   960        0.6649             nan     0.1000   -0.0007
   980        0.6640             nan     0.1000   -0.0012
  1000        0.6619             nan     0.1000   -0.0010
  1020        0.6600             nan     0.1000   -0.0006
  1040        0.6591             nan     0.1000   -0.0004
  1060        0.6594             nan     0.1000   -0.0010
  1080        0.6578             nan     0.1000   -0.0008
  1100        0.6563             nan     0.1000   -0.0009

- Fold05.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2627             nan     0.1000    0.0362
     2        1.2077             nan     0.1000    0.0275
     3        1.1602             nan     0.1000    0.0228
     4        1.1198             nan     0.1000    0.0204
     5        1.0835             nan     0.1000    0.0167
     6        1.0568             nan     0.1000    0.0138
     7        1.0319             nan     0.1000    0.0114
     8        1.0078             nan     0.1000    0.0107
     9        0.9906             nan     0.1000    0.0055
    10        0.9726             nan     0.1000    0.0029
    20        0.8771             nan     0.1000    0.0011
    40        0.8032             nan     0.1000   -0.0002
    60        0.7614             nan     0.1000    0.0002
    80        0.7342             nan     0.1000   -0.0005
   100        0.7131             nan     0.1000   -0.0001
   120        0.6946             nan     0.1000   -0.0020
   140        0.6805             nan     0.1000   -0.0006
   160        0.6704             nan     0.1000   -0.0015
   180        0.6558             nan     0.1000   -0.0012
   200        0.6426             nan     0.1000   -0.0016
   220        0.6323             nan     0.1000   -0.0009
   240        0.6205             nan     0.1000   -0.0005
   260        0.6123             nan     0.1000   -0.0005
   280        0.6047             nan     0.1000   -0.0004
   300        0.5941             nan     0.1000   -0.0012
   320        0.5864             nan     0.1000   -0.0003
   340        0.5768             nan     0.1000   -0.0012
   360        0.5686             nan     0.1000   -0.0005
   380        0.5602             nan     0.1000   -0.0017
   400        0.5514             nan     0.1000   -0.0008
   420        0.5443             nan     0.1000   -0.0002
   440        0.5359             nan     0.1000   -0.0004
   460        0.5295             nan     0.1000   -0.0012
   480        0.5245             nan     0.1000   -0.0018
   500        0.5173             nan     0.1000   -0.0005
   520        0.5128             nan     0.1000   -0.0008
   540        0.5063             nan     0.1000   -0.0013
   560        0.5007             nan     0.1000   -0.0006
   580        0.4969             nan     0.1000   -0.0013
   600        0.4899             nan     0.1000   -0.0002
   620        0.4861             nan     0.1000   -0.0006
   640        0.4805             nan     0.1000   -0.0006
   660        0.4741             nan     0.1000   -0.0009
   680        0.4693             nan     0.1000   -0.0005
   700        0.4637             nan     0.1000   -0.0017
   720        0.4597             nan     0.1000   -0.0007
   740        0.4568             nan     0.1000   -0.0013
   760        0.4506             nan     0.1000   -0.0009
   780        0.4466             nan     0.1000   -0.0012
   800        0.4431             nan     0.1000   -0.0012
   820        0.4385             nan     0.1000   -0.0005
   840        0.4339             nan     0.1000   -0.0004
   860        0.4295             nan     0.1000   -0.0010
   880        0.4256             nan     0.1000   -0.0001
   900        0.4235             nan     0.1000   -0.0018
   920        0.4185             nan     0.1000   -0.0010
   940        0.4149             nan     0.1000   -0.0006
   960        0.4120             nan     0.1000   -0.0009
   980        0.4094             nan     0.1000   -0.0006
  1000        0.4068             nan     0.1000   -0.0006
  1020        0.4024             nan     0.1000   -0.0013
  1040        0.3985             nan     0.1000   -0.0009
  1060        0.3962             nan     0.1000   -0.0010
  1080        0.3933             nan     0.1000   -0.0012
  1100        0.3902             nan     0.1000   -0.0010

- Fold05.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2550             nan     0.1000    0.0373
     2        1.1922             nan     0.1000    0.0319
     3        1.1422             nan     0.1000    0.0263
     4        1.0975             nan     0.1000    0.0204
     5        1.0615             nan     0.1000    0.0177
     6        1.0308             nan     0.1000    0.0151
     7        1.0038             nan     0.1000    0.0134
     8        0.9779             nan     0.1000    0.0121
     9        0.9581             nan     0.1000    0.0083
    10        0.9394             nan     0.1000    0.0094
    20        0.8361             nan     0.1000    0.0023
    40        0.7607             nan     0.1000   -0.0002
    60        0.7165             nan     0.1000   -0.0015
    80        0.6863             nan     0.1000   -0.0006
   100        0.6624             nan     0.1000   -0.0021
   120        0.6385             nan     0.1000   -0.0015
   140        0.6170             nan     0.1000   -0.0024
   160        0.5985             nan     0.1000   -0.0007
   180        0.5820             nan     0.1000   -0.0005
   200        0.5633             nan     0.1000   -0.0008
   220        0.5507             nan     0.1000   -0.0020
   240        0.5374             nan     0.1000   -0.0006
   260        0.5252             nan     0.1000   -0.0007
   280        0.5133             nan     0.1000   -0.0013
   300        0.5001             nan     0.1000   -0.0021
   320        0.4845             nan     0.1000   -0.0009
   340        0.4756             nan     0.1000   -0.0009
   360        0.4673             nan     0.1000   -0.0014
   380        0.4566             nan     0.1000   -0.0008
   400        0.4468             nan     0.1000   -0.0017
   420        0.4377             nan     0.1000   -0.0006
   440        0.4295             nan     0.1000   -0.0009
   460        0.4209             nan     0.1000   -0.0005
   480        0.4133             nan     0.1000   -0.0010
   500        0.4025             nan     0.1000   -0.0005
   520        0.3947             nan     0.1000   -0.0013
   540        0.3874             nan     0.1000   -0.0011
   560        0.3815             nan     0.1000   -0.0018
   580        0.3745             nan     0.1000   -0.0007
   600        0.3708             nan     0.1000   -0.0009
   620        0.3641             nan     0.1000   -0.0019
   640        0.3568             nan     0.1000   -0.0007
   660        0.3522             nan     0.1000   -0.0005
   680        0.3460             nan     0.1000   -0.0012
   700        0.3410             nan     0.1000   -0.0008
   720        0.3359             nan     0.1000   -0.0011
   740        0.3307             nan     0.1000   -0.0006
   760        0.3253             nan     0.1000   -0.0007
   780        0.3205             nan     0.1000   -0.0008
   800        0.3156             nan     0.1000   -0.0013
   820        0.3123             nan     0.1000   -0.0007
   840        0.3075             nan     0.1000   -0.0011
   860        0.3024             nan     0.1000   -0.0005
   880        0.2982             nan     0.1000   -0.0008
   900        0.2950             nan     0.1000   -0.0013
   920        0.2908             nan     0.1000   -0.0007
   940        0.2863             nan     0.1000   -0.0006
   960        0.2834             nan     0.1000   -0.0011
   980        0.2799             nan     0.1000   -0.0013
  1000        0.2773             nan     0.1000   -0.0005
  1020        0.2739             nan     0.1000   -0.0008
  1040        0.2701             nan     0.1000   -0.0008
  1060        0.2663             nan     0.1000   -0.0005
  1080        0.2633             nan     0.1000   -0.0003
  1100        0.2613             nan     0.1000   -0.0009

- Fold05.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3257             nan     0.0100    0.0029
     2        1.3195             nan     0.0100    0.0031
     3        1.3132             nan     0.0100    0.0030
     4        1.3072             nan     0.0100    0.0029
     5        1.3015             nan     0.0100    0.0029
     6        1.2954             nan     0.0100    0.0027
     7        1.2902             nan     0.0100    0.0026
     8        1.2852             nan     0.0100    0.0025
     9        1.2800             nan     0.0100    0.0026
    10        1.2744             nan     0.0100    0.0026
    20        1.2292             nan     0.0100    0.0020
    40        1.1564             nan     0.0100    0.0015
    60        1.1069             nan     0.0100    0.0010
    80        1.0701             nan     0.0100    0.0007
   100        1.0397             nan     0.0100    0.0006
   120        1.0151             nan     0.0100    0.0005
   140        0.9949             nan     0.0100    0.0004
   160        0.9767             nan     0.0100    0.0004
   180        0.9604             nan     0.0100    0.0001
   200        0.9469             nan     0.0100    0.0002
   220        0.9343             nan     0.0100    0.0002
   240        0.9233             nan     0.0100    0.0002
   260        0.9133             nan     0.0100    0.0002
   280        0.9049             nan     0.0100    0.0002
   300        0.8970             nan     0.0100    0.0000
   320        0.8895             nan     0.0100    0.0001
   340        0.8829             nan     0.0100    0.0001
   360        0.8770             nan     0.0100    0.0000
   380        0.8711             nan     0.0100    0.0001
   400        0.8656             nan     0.0100    0.0000
   420        0.8608             nan     0.0100    0.0000
   440        0.8561             nan     0.0100   -0.0000
   460        0.8516             nan     0.0100    0.0000
   480        0.8475             nan     0.0100   -0.0000
   500        0.8435             nan     0.0100   -0.0000
   520        0.8396             nan     0.0100    0.0000
   540        0.8360             nan     0.0100   -0.0000
   560        0.8325             nan     0.0100    0.0000
   580        0.8289             nan     0.0100    0.0000
   600        0.8255             nan     0.0100    0.0000
   620        0.8225             nan     0.0100    0.0000
   640        0.8196             nan     0.0100   -0.0001
   660        0.8169             nan     0.0100   -0.0000
   680        0.8142             nan     0.0100    0.0000
   700        0.8117             nan     0.0100   -0.0001
   720        0.8094             nan     0.0100   -0.0000
   740        0.8071             nan     0.0100   -0.0000
   760        0.8048             nan     0.0100   -0.0000
   780        0.8023             nan     0.0100   -0.0000
   800        0.8000             nan     0.0100   -0.0000
   820        0.7979             nan     0.0100   -0.0000
   840        0.7958             nan     0.0100   -0.0000
   860        0.7938             nan     0.0100   -0.0000
   880        0.7920             nan     0.0100   -0.0000
   900        0.7902             nan     0.0100   -0.0000
   920        0.7884             nan     0.0100    0.0000
   940        0.7866             nan     0.0100   -0.0001
   960        0.7848             nan     0.0100   -0.0001
   980        0.7832             nan     0.0100   -0.0000
  1000        0.7815             nan     0.0100   -0.0000
  1020        0.7798             nan     0.0100   -0.0000
  1040        0.7784             nan     0.0100   -0.0001
  1060        0.7771             nan     0.0100   -0.0000
  1080        0.7756             nan     0.0100   -0.0001
  1100        0.7742             nan     0.0100   -0.0001

- Fold06.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3239             nan     0.0100    0.0037
     2        1.3166             nan     0.0100    0.0038
     3        1.3092             nan     0.0100    0.0036
     4        1.3018             nan     0.0100    0.0036
     5        1.2944             nan     0.0100    0.0037
     6        1.2871             nan     0.0100    0.0035
     7        1.2803             nan     0.0100    0.0032
     8        1.2732             nan     0.0100    0.0033
     9        1.2672             nan     0.0100    0.0029
    10        1.2606             nan     0.0100    0.0033
    20        1.2019             nan     0.0100    0.0027
    40        1.1139             nan     0.0100    0.0017
    60        1.0470             nan     0.0100    0.0013
    80        0.9984             nan     0.0100    0.0010
   100        0.9601             nan     0.0100    0.0008
   120        0.9311             nan     0.0100    0.0006
   140        0.9084             nan     0.0100    0.0004
   160        0.8893             nan     0.0100    0.0002
   180        0.8739             nan     0.0100    0.0002
   200        0.8604             nan     0.0100    0.0002
   220        0.8498             nan     0.0100    0.0002
   240        0.8397             nan     0.0100    0.0002
   260        0.8302             nan     0.0100    0.0001
   280        0.8213             nan     0.0100   -0.0000
   300        0.8131             nan     0.0100    0.0001
   320        0.8056             nan     0.0100    0.0001
   340        0.7992             nan     0.0100    0.0001
   360        0.7932             nan     0.0100    0.0000
   380        0.7875             nan     0.0100    0.0000
   400        0.7816             nan     0.0100    0.0000
   420        0.7762             nan     0.0100    0.0000
   440        0.7717             nan     0.0100   -0.0000
   460        0.7671             nan     0.0100   -0.0001
   480        0.7627             nan     0.0100   -0.0000
   500        0.7584             nan     0.0100    0.0000
   520        0.7542             nan     0.0100   -0.0000
   540        0.7506             nan     0.0100   -0.0000
   560        0.7472             nan     0.0100   -0.0000
   580        0.7439             nan     0.0100   -0.0001
   600        0.7400             nan     0.0100   -0.0000
   620        0.7372             nan     0.0100   -0.0000
   640        0.7342             nan     0.0100   -0.0001
   660        0.7308             nan     0.0100   -0.0000
   680        0.7277             nan     0.0100    0.0000
   700        0.7247             nan     0.0100   -0.0000
   720        0.7217             nan     0.0100   -0.0001
   740        0.7192             nan     0.0100   -0.0000
   760        0.7163             nan     0.0100   -0.0000
   780        0.7136             nan     0.0100   -0.0000
   800        0.7111             nan     0.0100   -0.0001
   820        0.7088             nan     0.0100   -0.0000
   840        0.7063             nan     0.0100   -0.0002
   860        0.7039             nan     0.0100   -0.0000
   880        0.7016             nan     0.0100    0.0000
   900        0.6996             nan     0.0100   -0.0000
   920        0.6975             nan     0.0100   -0.0001
   940        0.6950             nan     0.0100   -0.0000
   960        0.6928             nan     0.0100   -0.0001
   980        0.6909             nan     0.0100   -0.0001
  1000        0.6888             nan     0.0100   -0.0000
  1020        0.6866             nan     0.0100   -0.0001
  1040        0.6847             nan     0.0100   -0.0000
  1060        0.6829             nan     0.0100   -0.0001
  1080        0.6812             nan     0.0100   -0.0001
  1100        0.6793             nan     0.0100   -0.0001

- Fold06.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3237             nan     0.0100    0.0039
     2        1.3154             nan     0.0100    0.0042
     3        1.3072             nan     0.0100    0.0038
     4        1.2986             nan     0.0100    0.0040
     5        1.2908             nan     0.0100    0.0038
     6        1.2837             nan     0.0100    0.0036
     7        1.2758             nan     0.0100    0.0036
     8        1.2684             nan     0.0100    0.0037
     9        1.2615             nan     0.0100    0.0032
    10        1.2551             nan     0.0100    0.0031
    20        1.1916             nan     0.0100    0.0028
    40        1.0904             nan     0.0100    0.0020
    60        1.0186             nan     0.0100    0.0015
    80        0.9660             nan     0.0100    0.0011
   100        0.9263             nan     0.0100    0.0007
   120        0.8949             nan     0.0100    0.0006
   140        0.8712             nan     0.0100    0.0005
   160        0.8516             nan     0.0100    0.0002
   180        0.8350             nan     0.0100    0.0003
   200        0.8206             nan     0.0100    0.0003
   220        0.8083             nan     0.0100   -0.0000
   240        0.7965             nan     0.0100   -0.0000
   260        0.7863             nan     0.0100    0.0001
   280        0.7771             nan     0.0100    0.0001
   300        0.7685             nan     0.0100    0.0001
   320        0.7610             nan     0.0100   -0.0000
   340        0.7537             nan     0.0100   -0.0001
   360        0.7474             nan     0.0100   -0.0000
   380        0.7409             nan     0.0100    0.0000
   400        0.7356             nan     0.0100   -0.0000
   420        0.7302             nan     0.0100   -0.0002
   440        0.7251             nan     0.0100   -0.0002
   460        0.7201             nan     0.0100   -0.0001
   480        0.7155             nan     0.0100    0.0000
   500        0.7111             nan     0.0100   -0.0001
   520        0.7068             nan     0.0100   -0.0002
   540        0.7025             nan     0.0100   -0.0001
   560        0.6979             nan     0.0100   -0.0001
   580        0.6938             nan     0.0100   -0.0001
   600        0.6899             nan     0.0100   -0.0001
   620        0.6862             nan     0.0100   -0.0000
   640        0.6823             nan     0.0100   -0.0001
   660        0.6791             nan     0.0100   -0.0001
   680        0.6759             nan     0.0100   -0.0001
   700        0.6725             nan     0.0100    0.0000
   720        0.6695             nan     0.0100   -0.0002
   740        0.6664             nan     0.0100   -0.0001
   760        0.6631             nan     0.0100   -0.0000
   780        0.6601             nan     0.0100   -0.0001
   800        0.6573             nan     0.0100   -0.0001
   820        0.6545             nan     0.0100   -0.0001
   840        0.6513             nan     0.0100   -0.0001
   860        0.6486             nan     0.0100   -0.0001
   880        0.6461             nan     0.0100   -0.0002
   900        0.6435             nan     0.0100   -0.0001
   920        0.6407             nan     0.0100   -0.0001
   940        0.6378             nan     0.0100   -0.0001
   960        0.6352             nan     0.0100   -0.0001
   980        0.6324             nan     0.0100   -0.0002
  1000        0.6296             nan     0.0100   -0.0001
  1020        0.6271             nan     0.0100   -0.0001
  1040        0.6244             nan     0.0100   -0.0000
  1060        0.6217             nan     0.0100   -0.0001
  1080        0.6194             nan     0.0100   -0.0001
  1100        0.6166             nan     0.0100   -0.0001

- Fold06.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2770             nan     0.1000    0.0276
     2        1.2292             nan     0.1000    0.0252
     3        1.1888             nan     0.1000    0.0205
     4        1.1499             nan     0.1000    0.0157
     5        1.1229             nan     0.1000    0.0130
     6        1.1034             nan     0.1000    0.0108
     7        1.0833             nan     0.1000    0.0075
     8        1.0651             nan     0.1000    0.0089
     9        1.0510             nan     0.1000    0.0059
    10        1.0403             nan     0.1000    0.0048
    20        0.9458             nan     0.1000    0.0027
    40        0.8663             nan     0.1000   -0.0003
    60        0.8270             nan     0.1000    0.0007
    80        0.7990             nan     0.1000   -0.0003
   100        0.7795             nan     0.1000   -0.0002
   120        0.7675             nan     0.1000   -0.0005
   140        0.7562             nan     0.1000   -0.0004
   160        0.7478             nan     0.1000    0.0000
   180        0.7397             nan     0.1000   -0.0004
   200        0.7343             nan     0.1000   -0.0004
   220        0.7297             nan     0.1000   -0.0002
   240        0.7239             nan     0.1000   -0.0008
   260        0.7171             nan     0.1000   -0.0002
   280        0.7124             nan     0.1000   -0.0002
   300        0.7080             nan     0.1000   -0.0007
   320        0.7029             nan     0.1000   -0.0003
   340        0.6995             nan     0.1000   -0.0003
   360        0.6971             nan     0.1000   -0.0007
   380        0.6928             nan     0.1000   -0.0007
   400        0.6891             nan     0.1000   -0.0008
   420        0.6872             nan     0.1000   -0.0010
   440        0.6840             nan     0.1000   -0.0002
   460        0.6821             nan     0.1000   -0.0003
   480        0.6784             nan     0.1000   -0.0006
   500        0.6747             nan     0.1000   -0.0008
   520        0.6712             nan     0.1000   -0.0012
   540        0.6686             nan     0.1000   -0.0011
   560        0.6673             nan     0.1000   -0.0008
   580        0.6657             nan     0.1000   -0.0004
   600        0.6630             nan     0.1000   -0.0008
   620        0.6605             nan     0.1000   -0.0003
   640        0.6586             nan     0.1000   -0.0006
   660        0.6575             nan     0.1000   -0.0014
   680        0.6565             nan     0.1000   -0.0005
   700        0.6544             nan     0.1000   -0.0008
   720        0.6519             nan     0.1000   -0.0009
   740        0.6512             nan     0.1000   -0.0010
   760        0.6486             nan     0.1000   -0.0004
   780        0.6483             nan     0.1000   -0.0017
   800        0.6468             nan     0.1000   -0.0010
   820        0.6441             nan     0.1000   -0.0006
   840        0.6416             nan     0.1000   -0.0008
   860        0.6384             nan     0.1000   -0.0003
   880        0.6379             nan     0.1000   -0.0006
   900        0.6346             nan     0.1000   -0.0005
   920        0.6337             nan     0.1000   -0.0010
   940        0.6319             nan     0.1000   -0.0006
   960        0.6298             nan     0.1000   -0.0011
   980        0.6280             nan     0.1000   -0.0005
  1000        0.6264             nan     0.1000   -0.0007
  1020        0.6249             nan     0.1000   -0.0002
  1040        0.6224             nan     0.1000   -0.0007
  1060        0.6214             nan     0.1000   -0.0007
  1080        0.6200             nan     0.1000   -0.0008
  1100        0.6179             nan     0.1000   -0.0005

- Fold06.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2547             nan     0.1000    0.0355
     2        1.1966             nan     0.1000    0.0294
     3        1.1518             nan     0.1000    0.0200
     4        1.1099             nan     0.1000    0.0221
     5        1.0726             nan     0.1000    0.0177
     6        1.0414             nan     0.1000    0.0139
     7        1.0163             nan     0.1000    0.0115
     8        0.9900             nan     0.1000    0.0126
     9        0.9710             nan     0.1000    0.0093
    10        0.9522             nan     0.1000    0.0084
    20        0.8534             nan     0.1000    0.0013
    40        0.7749             nan     0.1000    0.0002
    60        0.7327             nan     0.1000   -0.0000
    80        0.7023             nan     0.1000    0.0006
   100        0.6842             nan     0.1000   -0.0009
   120        0.6673             nan     0.1000   -0.0006
   140        0.6534             nan     0.1000   -0.0012
   160        0.6419             nan     0.1000   -0.0017
   180        0.6300             nan     0.1000   -0.0007
   200        0.6203             nan     0.1000   -0.0006
   220        0.6098             nan     0.1000   -0.0011
   240        0.5977             nan     0.1000    0.0002
   260        0.5880             nan     0.1000   -0.0001
   280        0.5800             nan     0.1000   -0.0013
   300        0.5722             nan     0.1000   -0.0010
   320        0.5627             nan     0.1000   -0.0005
   340        0.5534             nan     0.1000   -0.0011
   360        0.5436             nan     0.1000   -0.0019
   380        0.5359             nan     0.1000   -0.0007
   400        0.5280             nan     0.1000   -0.0014
   420        0.5193             nan     0.1000   -0.0004
   440        0.5141             nan     0.1000   -0.0005
   460        0.5071             nan     0.1000   -0.0006
   480        0.4997             nan     0.1000   -0.0017
   500        0.4945             nan     0.1000   -0.0008
   520        0.4887             nan     0.1000   -0.0015
   540        0.4822             nan     0.1000   -0.0008
   560        0.4764             nan     0.1000   -0.0010
   580        0.4713             nan     0.1000   -0.0013
   600        0.4664             nan     0.1000   -0.0008
   620        0.4619             nan     0.1000   -0.0015
   640        0.4562             nan     0.1000   -0.0012
   660        0.4515             nan     0.1000   -0.0009
   680        0.4460             nan     0.1000   -0.0006
   700        0.4430             nan     0.1000   -0.0008
   720        0.4398             nan     0.1000   -0.0010
   740        0.4343             nan     0.1000   -0.0016
   760        0.4314             nan     0.1000   -0.0010
   780        0.4263             nan     0.1000   -0.0006
   800        0.4229             nan     0.1000   -0.0009
   820        0.4194             nan     0.1000   -0.0008
   840        0.4167             nan     0.1000   -0.0008
   860        0.4130             nan     0.1000   -0.0009
   880        0.4092             nan     0.1000   -0.0011
   900        0.4060             nan     0.1000   -0.0005
   920        0.4027             nan     0.1000   -0.0007
   940        0.3989             nan     0.1000   -0.0014
   960        0.3957             nan     0.1000   -0.0010
   980        0.3904             nan     0.1000   -0.0001
  1000        0.3880             nan     0.1000   -0.0010
  1020        0.3854             nan     0.1000   -0.0013
  1040        0.3816             nan     0.1000   -0.0005
  1060        0.3787             nan     0.1000   -0.0003
  1080        0.3762             nan     0.1000   -0.0008
  1100        0.3734             nan     0.1000   -0.0009

- Fold06.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2528             nan     0.1000    0.0376
     2        1.1836             nan     0.1000    0.0329
     3        1.1320             nan     0.1000    0.0221
     4        1.0875             nan     0.1000    0.0222
     5        1.0464             nan     0.1000    0.0195
     6        1.0147             nan     0.1000    0.0170
     7        0.9852             nan     0.1000    0.0115
     8        0.9576             nan     0.1000    0.0120
     9        0.9353             nan     0.1000    0.0099
    10        0.9137             nan     0.1000    0.0091
    20        0.8108             nan     0.1000    0.0024
    40        0.7295             nan     0.1000   -0.0000
    60        0.6854             nan     0.1000   -0.0006
    80        0.6540             nan     0.1000   -0.0025
   100        0.6304             nan     0.1000   -0.0013
   120        0.6022             nan     0.1000   -0.0005
   140        0.5798             nan     0.1000   -0.0014
   160        0.5631             nan     0.1000   -0.0009
   180        0.5460             nan     0.1000   -0.0012
   200        0.5294             nan     0.1000    0.0001
   220        0.5145             nan     0.1000   -0.0013
   240        0.5015             nan     0.1000   -0.0005
   260        0.4908             nan     0.1000   -0.0021
   280        0.4812             nan     0.1000   -0.0024
   300        0.4712             nan     0.1000   -0.0012
   320        0.4586             nan     0.1000   -0.0006
   340        0.4492             nan     0.1000   -0.0006
   360        0.4390             nan     0.1000   -0.0013
   380        0.4292             nan     0.1000   -0.0013
   400        0.4215             nan     0.1000   -0.0005
   420        0.4154             nan     0.1000   -0.0009
   440        0.4073             nan     0.1000   -0.0012
   460        0.3994             nan     0.1000   -0.0012
   480        0.3916             nan     0.1000   -0.0013
   500        0.3844             nan     0.1000   -0.0011
   520        0.3765             nan     0.1000   -0.0014
   540        0.3692             nan     0.1000   -0.0009
   560        0.3635             nan     0.1000   -0.0010
   580        0.3557             nan     0.1000   -0.0014
   600        0.3514             nan     0.1000   -0.0012
   620        0.3443             nan     0.1000   -0.0010
   640        0.3396             nan     0.1000   -0.0008
   660        0.3345             nan     0.1000   -0.0010
   680        0.3303             nan     0.1000   -0.0011
   700        0.3256             nan     0.1000   -0.0015
   720        0.3185             nan     0.1000   -0.0009
   740        0.3126             nan     0.1000   -0.0009
   760        0.3075             nan     0.1000   -0.0007
   780        0.3037             nan     0.1000   -0.0009
   800        0.2979             nan     0.1000   -0.0004
   820        0.2934             nan     0.1000   -0.0011
   840        0.2900             nan     0.1000   -0.0015
   860        0.2859             nan     0.1000   -0.0013
   880        0.2815             nan     0.1000   -0.0010
   900        0.2779             nan     0.1000   -0.0012
   920        0.2726             nan     0.1000   -0.0005
   940        0.2691             nan     0.1000   -0.0006
   960        0.2655             nan     0.1000   -0.0008
   980        0.2635             nan     0.1000   -0.0006
  1000        0.2598             nan     0.1000   -0.0007
  1020        0.2560             nan     0.1000   -0.0007
  1040        0.2517             nan     0.1000   -0.0007
  1060        0.2486             nan     0.1000   -0.0006
  1080        0.2453             nan     0.1000   -0.0006
  1100        0.2428             nan     0.1000   -0.0011

- Fold06.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3253             nan     0.0100    0.0031
     2        1.3188             nan     0.0100    0.0031
     3        1.3128             nan     0.0100    0.0029
     4        1.3071             nan     0.0100    0.0030
     5        1.3011             nan     0.0100    0.0030
     6        1.2952             nan     0.0100    0.0028
     7        1.2891             nan     0.0100    0.0028
     8        1.2836             nan     0.0100    0.0025
     9        1.2785             nan     0.0100    0.0026
    10        1.2734             nan     0.0100    0.0027
    20        1.2247             nan     0.0100    0.0022
    40        1.1508             nan     0.0100    0.0015
    60        1.1004             nan     0.0100    0.0010
    80        1.0625             nan     0.0100    0.0008
   100        1.0323             nan     0.0100    0.0006
   120        1.0061             nan     0.0100    0.0006
   140        0.9845             nan     0.0100    0.0004
   160        0.9657             nan     0.0100    0.0002
   180        0.9499             nan     0.0100    0.0003
   200        0.9359             nan     0.0100    0.0003
   220        0.9239             nan     0.0100    0.0002
   240        0.9137             nan     0.0100    0.0001
   260        0.9042             nan     0.0100    0.0002
   280        0.8952             nan     0.0100    0.0000
   300        0.8880             nan     0.0100    0.0001
   320        0.8809             nan     0.0100    0.0001
   340        0.8747             nan     0.0100    0.0000
   360        0.8687             nan     0.0100    0.0000
   380        0.8637             nan     0.0100   -0.0000
   400        0.8582             nan     0.0100   -0.0000
   420        0.8533             nan     0.0100    0.0000
   440        0.8491             nan     0.0100    0.0000
   460        0.8452             nan     0.0100    0.0001
   480        0.8415             nan     0.0100    0.0000
   500        0.8376             nan     0.0100    0.0001
   520        0.8344             nan     0.0100   -0.0000
   540        0.8309             nan     0.0100    0.0000
   560        0.8277             nan     0.0100    0.0000
   580        0.8245             nan     0.0100   -0.0001
   600        0.8215             nan     0.0100   -0.0001
   620        0.8189             nan     0.0100   -0.0000
   640        0.8163             nan     0.0100   -0.0000
   660        0.8139             nan     0.0100   -0.0000
   680        0.8114             nan     0.0100    0.0000
   700        0.8092             nan     0.0100   -0.0001
   720        0.8071             nan     0.0100   -0.0001
   740        0.8049             nan     0.0100   -0.0000
   760        0.8030             nan     0.0100   -0.0001
   780        0.8012             nan     0.0100    0.0000
   800        0.7994             nan     0.0100   -0.0001
   820        0.7978             nan     0.0100   -0.0000
   840        0.7962             nan     0.0100   -0.0001
   860        0.7943             nan     0.0100    0.0000
   880        0.7927             nan     0.0100   -0.0000
   900        0.7911             nan     0.0100   -0.0001
   920        0.7895             nan     0.0100   -0.0000
   940        0.7880             nan     0.0100   -0.0000
   960        0.7864             nan     0.0100   -0.0000
   980        0.7848             nan     0.0100   -0.0000
  1000        0.7833             nan     0.0100   -0.0000
  1020        0.7819             nan     0.0100   -0.0000
  1040        0.7806             nan     0.0100   -0.0000
  1060        0.7794             nan     0.0100   -0.0000
  1080        0.7779             nan     0.0100   -0.0000
  1100        0.7766             nan     0.0100   -0.0000

- Fold07.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3236             nan     0.0100    0.0040
     2        1.3158             nan     0.0100    0.0041
     3        1.3085             nan     0.0100    0.0035
     4        1.3015             nan     0.0100    0.0033
     5        1.2939             nan     0.0100    0.0036
     6        1.2866             nan     0.0100    0.0035
     7        1.2796             nan     0.0100    0.0033
     8        1.2728             nan     0.0100    0.0033
     9        1.2657             nan     0.0100    0.0034
    10        1.2587             nan     0.0100    0.0033
    20        1.1978             nan     0.0100    0.0027
    40        1.1070             nan     0.0100    0.0020
    60        1.0416             nan     0.0100    0.0015
    80        0.9918             nan     0.0100    0.0009
   100        0.9536             nan     0.0100    0.0008
   120        0.9236             nan     0.0100    0.0004
   140        0.8998             nan     0.0100    0.0005
   160        0.8815             nan     0.0100    0.0004
   180        0.8667             nan     0.0100    0.0002
   200        0.8533             nan     0.0100    0.0003
   220        0.8425             nan     0.0100    0.0002
   240        0.8322             nan     0.0100    0.0001
   260        0.8236             nan     0.0100    0.0000
   280        0.8156             nan     0.0100    0.0001
   300        0.8088             nan     0.0100    0.0000
   320        0.8028             nan     0.0100   -0.0000
   340        0.7969             nan     0.0100   -0.0000
   360        0.7915             nan     0.0100    0.0000
   380        0.7863             nan     0.0100   -0.0000
   400        0.7815             nan     0.0100   -0.0001
   420        0.7768             nan     0.0100   -0.0000
   440        0.7727             nan     0.0100    0.0000
   460        0.7687             nan     0.0100   -0.0001
   480        0.7650             nan     0.0100   -0.0001
   500        0.7612             nan     0.0100   -0.0001
   520        0.7577             nan     0.0100   -0.0000
   540        0.7544             nan     0.0100   -0.0001
   560        0.7513             nan     0.0100   -0.0001
   580        0.7480             nan     0.0100   -0.0001
   600        0.7451             nan     0.0100   -0.0000
   620        0.7426             nan     0.0100   -0.0001
   640        0.7395             nan     0.0100   -0.0002
   660        0.7370             nan     0.0100   -0.0001
   680        0.7346             nan     0.0100   -0.0001
   700        0.7322             nan     0.0100   -0.0001
   720        0.7298             nan     0.0100   -0.0000
   740        0.7277             nan     0.0100   -0.0001
   760        0.7254             nan     0.0100   -0.0000
   780        0.7230             nan     0.0100   -0.0001
   800        0.7207             nan     0.0100   -0.0000
   820        0.7180             nan     0.0100   -0.0001
   840        0.7162             nan     0.0100   -0.0001
   860        0.7141             nan     0.0100   -0.0001
   880        0.7117             nan     0.0100   -0.0001
   900        0.7096             nan     0.0100   -0.0000
   920        0.7080             nan     0.0100   -0.0001
   940        0.7061             nan     0.0100   -0.0001
   960        0.7041             nan     0.0100   -0.0001
   980        0.7024             nan     0.0100   -0.0001
  1000        0.7003             nan     0.0100   -0.0000
  1020        0.6983             nan     0.0100   -0.0001
  1040        0.6966             nan     0.0100   -0.0001
  1060        0.6946             nan     0.0100    0.0000
  1080        0.6926             nan     0.0100   -0.0000
  1100        0.6908             nan     0.0100   -0.0000

- Fold07.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3235             nan     0.0100    0.0042
     2        1.3147             nan     0.0100    0.0044
     3        1.3065             nan     0.0100    0.0040
     4        1.2985             nan     0.0100    0.0038
     5        1.2908             nan     0.0100    0.0039
     6        1.2834             nan     0.0100    0.0035
     7        1.2761             nan     0.0100    0.0036
     8        1.2690             nan     0.0100    0.0033
     9        1.2615             nan     0.0100    0.0038
    10        1.2545             nan     0.0100    0.0034
    20        1.1889             nan     0.0100    0.0029
    40        1.0887             nan     0.0100    0.0020
    60        1.0135             nan     0.0100    0.0015
    80        0.9594             nan     0.0100    0.0011
   100        0.9204             nan     0.0100    0.0007
   120        0.8896             nan     0.0100    0.0005
   140        0.8660             nan     0.0100    0.0004
   160        0.8461             nan     0.0100    0.0003
   180        0.8302             nan     0.0100    0.0001
   200        0.8167             nan     0.0100    0.0003
   220        0.8059             nan     0.0100    0.0001
   240        0.7967             nan     0.0100    0.0001
   260        0.7874             nan     0.0100    0.0001
   280        0.7793             nan     0.0100   -0.0000
   300        0.7717             nan     0.0100   -0.0000
   320        0.7649             nan     0.0100    0.0001
   340        0.7583             nan     0.0100    0.0000
   360        0.7516             nan     0.0100   -0.0000
   380        0.7453             nan     0.0100   -0.0002
   400        0.7396             nan     0.0100   -0.0001
   420        0.7345             nan     0.0100   -0.0001
   440        0.7293             nan     0.0100   -0.0001
   460        0.7243             nan     0.0100   -0.0000
   480        0.7195             nan     0.0100    0.0000
   500        0.7159             nan     0.0100   -0.0002
   520        0.7111             nan     0.0100   -0.0002
   540        0.7073             nan     0.0100   -0.0000
   560        0.7036             nan     0.0100   -0.0000
   580        0.7001             nan     0.0100   -0.0001
   600        0.6967             nan     0.0100   -0.0002
   620        0.6933             nan     0.0100    0.0000
   640        0.6901             nan     0.0100   -0.0001
   660        0.6866             nan     0.0100   -0.0002
   680        0.6831             nan     0.0100   -0.0001
   700        0.6802             nan     0.0100   -0.0001
   720        0.6769             nan     0.0100    0.0000
   740        0.6736             nan     0.0100   -0.0000
   760        0.6699             nan     0.0100   -0.0001
   780        0.6665             nan     0.0100   -0.0001
   800        0.6636             nan     0.0100   -0.0002
   820        0.6609             nan     0.0100    0.0000
   840        0.6580             nan     0.0100   -0.0002
   860        0.6553             nan     0.0100   -0.0001
   880        0.6522             nan     0.0100   -0.0001
   900        0.6501             nan     0.0100   -0.0002
   920        0.6473             nan     0.0100   -0.0001
   940        0.6449             nan     0.0100   -0.0001
   960        0.6423             nan     0.0100   -0.0001
   980        0.6394             nan     0.0100   -0.0000
  1000        0.6374             nan     0.0100   -0.0001
  1020        0.6348             nan     0.0100   -0.0001
  1040        0.6322             nan     0.0100   -0.0001
  1060        0.6294             nan     0.0100   -0.0001
  1080        0.6275             nan     0.0100   -0.0001
  1100        0.6251             nan     0.0100   -0.0001

- Fold07.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2691             nan     0.1000    0.0303
     2        1.2186             nan     0.1000    0.0244
     3        1.1752             nan     0.1000    0.0191
     4        1.1435             nan     0.1000    0.0149
     5        1.1146             nan     0.1000    0.0135
     6        1.0893             nan     0.1000    0.0106
     7        1.0701             nan     0.1000    0.0085
     8        1.0518             nan     0.1000    0.0078
     9        1.0337             nan     0.1000    0.0073
    10        1.0208             nan     0.1000    0.0067
    20        0.9263             nan     0.1000    0.0019
    40        0.8544             nan     0.1000   -0.0001
    60        0.8236             nan     0.1000   -0.0003
    80        0.8026             nan     0.1000   -0.0000
   100        0.7841             nan     0.1000   -0.0007
   120        0.7731             nan     0.1000   -0.0006
   140        0.7636             nan     0.1000   -0.0009
   160        0.7544             nan     0.1000   -0.0013
   180        0.7482             nan     0.1000   -0.0011
   200        0.7410             nan     0.1000   -0.0007
   220        0.7362             nan     0.1000   -0.0001
   240        0.7311             nan     0.1000   -0.0006
   260        0.7270             nan     0.1000   -0.0012
   280        0.7230             nan     0.1000   -0.0009
   300        0.7189             nan     0.1000   -0.0009
   320        0.7133             nan     0.1000   -0.0003
   340        0.7105             nan     0.1000   -0.0006
   360        0.7071             nan     0.1000   -0.0014
   380        0.7034             nan     0.1000   -0.0004
   400        0.7002             nan     0.1000   -0.0004
   420        0.6982             nan     0.1000   -0.0012
   440        0.6950             nan     0.1000   -0.0006
   460        0.6919             nan     0.1000   -0.0006
   480        0.6888             nan     0.1000   -0.0008
   500        0.6850             nan     0.1000   -0.0009
   520        0.6827             nan     0.1000   -0.0013
   540        0.6808             nan     0.1000   -0.0006
   560        0.6763             nan     0.1000   -0.0004
   580        0.6734             nan     0.1000   -0.0003
   600        0.6709             nan     0.1000   -0.0009
   620        0.6696             nan     0.1000   -0.0010
   640        0.6672             nan     0.1000   -0.0006
   660        0.6648             nan     0.1000   -0.0007
   680        0.6620             nan     0.1000   -0.0012
   700        0.6596             nan     0.1000   -0.0009
   720        0.6572             nan     0.1000   -0.0005
   740        0.6561             nan     0.1000   -0.0005
   760        0.6549             nan     0.1000   -0.0007
   780        0.6524             nan     0.1000   -0.0007
   800        0.6507             nan     0.1000   -0.0009
   820        0.6503             nan     0.1000   -0.0011
   840        0.6482             nan     0.1000   -0.0007
   860        0.6473             nan     0.1000   -0.0007
   880        0.6460             nan     0.1000   -0.0005
   900        0.6447             nan     0.1000   -0.0014
   920        0.6435             nan     0.1000   -0.0011
   940        0.6421             nan     0.1000   -0.0012
   960        0.6406             nan     0.1000   -0.0008
   980        0.6389             nan     0.1000   -0.0009
  1000        0.6375             nan     0.1000   -0.0015
  1020        0.6372             nan     0.1000   -0.0006
  1040        0.6364             nan     0.1000   -0.0007
  1060        0.6345             nan     0.1000   -0.0008
  1080        0.6342             nan     0.1000   -0.0012
  1100        0.6325             nan     0.1000   -0.0002

- Fold07.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2511             nan     0.1000    0.0352
     2        1.1947             nan     0.1000    0.0300
     3        1.1390             nan     0.1000    0.0226
     4        1.0988             nan     0.1000    0.0174
     5        1.0634             nan     0.1000    0.0166
     6        1.0352             nan     0.1000    0.0144
     7        1.0092             nan     0.1000    0.0132
     8        0.9856             nan     0.1000    0.0109
     9        0.9660             nan     0.1000    0.0089
    10        0.9472             nan     0.1000    0.0083
    20        0.8536             nan     0.1000    0.0015
    40        0.7804             nan     0.1000    0.0013
    60        0.7480             nan     0.1000   -0.0021
    80        0.7230             nan     0.1000   -0.0008
   100        0.6999             nan     0.1000   -0.0001
   120        0.6854             nan     0.1000   -0.0012
   140        0.6726             nan     0.1000   -0.0014
   160        0.6590             nan     0.1000   -0.0013
   180        0.6449             nan     0.1000   -0.0008
   200        0.6295             nan     0.1000   -0.0013
   220        0.6175             nan     0.1000   -0.0011
   240        0.6077             nan     0.1000   -0.0017
   260        0.5972             nan     0.1000   -0.0009
   280        0.5864             nan     0.1000   -0.0001
   300        0.5772             nan     0.1000   -0.0010
   320        0.5682             nan     0.1000   -0.0005
   340        0.5611             nan     0.1000   -0.0009
   360        0.5552             nan     0.1000   -0.0007
   380        0.5464             nan     0.1000   -0.0011
   400        0.5401             nan     0.1000   -0.0005
   420        0.5322             nan     0.1000   -0.0007
   440        0.5273             nan     0.1000   -0.0010
   460        0.5225             nan     0.1000   -0.0017
   480        0.5156             nan     0.1000   -0.0011
   500        0.5106             nan     0.1000   -0.0007
   520        0.5059             nan     0.1000   -0.0007
   540        0.5023             nan     0.1000   -0.0007
   560        0.4981             nan     0.1000   -0.0007
   580        0.4927             nan     0.1000   -0.0007
   600        0.4866             nan     0.1000   -0.0007
   620        0.4823             nan     0.1000   -0.0005
   640        0.4780             nan     0.1000   -0.0006
   660        0.4738             nan     0.1000   -0.0007
   680        0.4693             nan     0.1000   -0.0009
   700        0.4634             nan     0.1000   -0.0019
   720        0.4581             nan     0.1000   -0.0009
   740        0.4541             nan     0.1000   -0.0007
   760        0.4493             nan     0.1000   -0.0014
   780        0.4435             nan     0.1000   -0.0010
   800        0.4407             nan     0.1000   -0.0009
   820        0.4373             nan     0.1000   -0.0011
   840        0.4329             nan     0.1000   -0.0009
   860        0.4281             nan     0.1000   -0.0011
   880        0.4242             nan     0.1000   -0.0006
   900        0.4208             nan     0.1000   -0.0008
   920        0.4172             nan     0.1000   -0.0007
   940        0.4132             nan     0.1000   -0.0013
   960        0.4100             nan     0.1000   -0.0015
   980        0.4069             nan     0.1000   -0.0019
  1000        0.4039             nan     0.1000   -0.0015
  1020        0.4003             nan     0.1000   -0.0007
  1040        0.3977             nan     0.1000   -0.0006
  1060        0.3941             nan     0.1000   -0.0002
  1080        0.3915             nan     0.1000   -0.0007
  1100        0.3882             nan     0.1000   -0.0010

- Fold07.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2474             nan     0.1000    0.0385
     2        1.1788             nan     0.1000    0.0343
     3        1.1258             nan     0.1000    0.0250
     4        1.0770             nan     0.1000    0.0194
     5        1.0380             nan     0.1000    0.0174
     6        1.0036             nan     0.1000    0.0126
     7        0.9734             nan     0.1000    0.0141
     8        0.9497             nan     0.1000    0.0108
     9        0.9283             nan     0.1000    0.0079
    10        0.9092             nan     0.1000    0.0096
    20        0.8129             nan     0.1000    0.0034
    40        0.7388             nan     0.1000   -0.0001
    60        0.6964             nan     0.1000   -0.0016
    80        0.6696             nan     0.1000   -0.0012
   100        0.6426             nan     0.1000   -0.0011
   120        0.6215             nan     0.1000   -0.0014
   140        0.5992             nan     0.1000   -0.0006
   160        0.5826             nan     0.1000   -0.0020
   180        0.5639             nan     0.1000   -0.0017
   200        0.5489             nan     0.1000   -0.0007
   220        0.5343             nan     0.1000   -0.0012
   240        0.5223             nan     0.1000   -0.0007
   260        0.5090             nan     0.1000   -0.0013
   280        0.4971             nan     0.1000   -0.0007
   300        0.4873             nan     0.1000   -0.0010
   320        0.4780             nan     0.1000   -0.0017
   340        0.4657             nan     0.1000   -0.0014
   360        0.4555             nan     0.1000   -0.0010
   380        0.4471             nan     0.1000   -0.0012
   400        0.4396             nan     0.1000   -0.0019
   420        0.4307             nan     0.1000   -0.0015
   440        0.4236             nan     0.1000   -0.0011
   460        0.4155             nan     0.1000   -0.0006
   480        0.4090             nan     0.1000   -0.0008
   500        0.4017             nan     0.1000   -0.0010
   520        0.3943             nan     0.1000   -0.0010
   540        0.3858             nan     0.1000   -0.0011
   560        0.3795             nan     0.1000   -0.0012
   580        0.3713             nan     0.1000   -0.0010
   600        0.3630             nan     0.1000   -0.0007
   620        0.3571             nan     0.1000   -0.0006
   640        0.3503             nan     0.1000   -0.0014
   660        0.3450             nan     0.1000   -0.0008
   680        0.3411             nan     0.1000   -0.0009
   700        0.3356             nan     0.1000   -0.0014
   720        0.3267             nan     0.1000   -0.0007
   740        0.3219             nan     0.1000   -0.0005
   760        0.3164             nan     0.1000   -0.0008
   780        0.3105             nan     0.1000   -0.0004
   800        0.3043             nan     0.1000   -0.0005
   820        0.3012             nan     0.1000   -0.0013
   840        0.2952             nan     0.1000   -0.0015
   860        0.2901             nan     0.1000   -0.0007
   880        0.2849             nan     0.1000   -0.0006
   900        0.2820             nan     0.1000   -0.0004
   920        0.2790             nan     0.1000   -0.0006
   940        0.2750             nan     0.1000   -0.0004
   960        0.2724             nan     0.1000   -0.0007
   980        0.2692             nan     0.1000   -0.0011
  1000        0.2661             nan     0.1000   -0.0005
  1020        0.2636             nan     0.1000   -0.0016
  1040        0.2617             nan     0.1000   -0.0006
  1060        0.2584             nan     0.1000   -0.0008
  1080        0.2550             nan     0.1000   -0.0006
  1100        0.2513             nan     0.1000   -0.0005

- Fold07.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3259             nan     0.0100    0.0030
     2        1.3199             nan     0.0100    0.0030
     3        1.3137             nan     0.0100    0.0029
     4        1.3075             nan     0.0100    0.0029
     5        1.3016             nan     0.0100    0.0028
     6        1.2963             nan     0.0100    0.0028
     7        1.2910             nan     0.0100    0.0027
     8        1.2856             nan     0.0100    0.0027
     9        1.2791             nan     0.0100    0.0027
    10        1.2739             nan     0.0100    0.0026
    20        1.2257             nan     0.0100    0.0021
    40        1.1550             nan     0.0100    0.0015
    60        1.1050             nan     0.0100    0.0011
    80        1.0663             nan     0.0100    0.0007
   100        1.0353             nan     0.0100    0.0006
   120        1.0103             nan     0.0100    0.0005
   140        0.9886             nan     0.0100    0.0003
   160        0.9705             nan     0.0100    0.0004
   180        0.9551             nan     0.0100    0.0003
   200        0.9415             nan     0.0100    0.0002
   220        0.9290             nan     0.0100    0.0002
   240        0.9175             nan     0.0100    0.0002
   260        0.9078             nan     0.0100    0.0002
   280        0.8987             nan     0.0100    0.0002
   300        0.8910             nan     0.0100    0.0000
   320        0.8838             nan     0.0100    0.0001
   340        0.8773             nan     0.0100    0.0001
   360        0.8710             nan     0.0100    0.0001
   380        0.8657             nan     0.0100    0.0000
   400        0.8599             nan     0.0100    0.0001
   420        0.8552             nan     0.0100    0.0000
   440        0.8504             nan     0.0100    0.0000
   460        0.8460             nan     0.0100   -0.0000
   480        0.8417             nan     0.0100    0.0001
   500        0.8375             nan     0.0100   -0.0000
   520        0.8336             nan     0.0100   -0.0001
   540        0.8298             nan     0.0100    0.0001
   560        0.8263             nan     0.0100   -0.0001
   580        0.8230             nan     0.0100    0.0001
   600        0.8197             nan     0.0100   -0.0000
   620        0.8164             nan     0.0100   -0.0000
   640        0.8133             nan     0.0100   -0.0001
   660        0.8104             nan     0.0100    0.0000
   680        0.8079             nan     0.0100   -0.0000
   700        0.8054             nan     0.0100    0.0000
   720        0.8031             nan     0.0100    0.0000
   740        0.8006             nan     0.0100   -0.0000
   760        0.7985             nan     0.0100   -0.0001
   780        0.7962             nan     0.0100   -0.0000
   800        0.7939             nan     0.0100    0.0000
   820        0.7919             nan     0.0100    0.0000
   840        0.7897             nan     0.0100   -0.0001
   860        0.7878             nan     0.0100   -0.0001
   880        0.7859             nan     0.0100   -0.0000
   900        0.7839             nan     0.0100   -0.0001
   920        0.7821             nan     0.0100   -0.0000
   940        0.7802             nan     0.0100   -0.0001
   960        0.7787             nan     0.0100   -0.0000
   980        0.7772             nan     0.0100    0.0000
  1000        0.7756             nan     0.0100   -0.0000
  1020        0.7740             nan     0.0100   -0.0001
  1040        0.7725             nan     0.0100   -0.0001
  1060        0.7708             nan     0.0100   -0.0000
  1080        0.7694             nan     0.0100   -0.0000
  1100        0.7681             nan     0.0100   -0.0001

- Fold08.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0038
     2        1.3168             nan     0.0100    0.0034
     3        1.3095             nan     0.0100    0.0037
     4        1.3020             nan     0.0100    0.0036
     5        1.2952             nan     0.0100    0.0034
     6        1.2886             nan     0.0100    0.0034
     7        1.2816             nan     0.0100    0.0035
     8        1.2744             nan     0.0100    0.0032
     9        1.2680             nan     0.0100    0.0034
    10        1.2620             nan     0.0100    0.0030
    20        1.2047             nan     0.0100    0.0024
    40        1.1138             nan     0.0100    0.0020
    60        1.0469             nan     0.0100    0.0012
    80        0.9978             nan     0.0100    0.0011
   100        0.9603             nan     0.0100    0.0008
   120        0.9308             nan     0.0100    0.0006
   140        0.9059             nan     0.0100    0.0004
   160        0.8870             nan     0.0100    0.0002
   180        0.8704             nan     0.0100    0.0003
   200        0.8569             nan     0.0100    0.0003
   220        0.8451             nan     0.0100    0.0003
   240        0.8336             nan     0.0100    0.0001
   260        0.8241             nan     0.0100   -0.0000
   280        0.8162             nan     0.0100    0.0002
   300        0.8087             nan     0.0100    0.0000
   320        0.8022             nan     0.0100    0.0000
   340        0.7954             nan     0.0100    0.0000
   360        0.7889             nan     0.0100    0.0000
   380        0.7836             nan     0.0100   -0.0000
   400        0.7781             nan     0.0100    0.0000
   420        0.7726             nan     0.0100    0.0001
   440        0.7677             nan     0.0100    0.0000
   460        0.7635             nan     0.0100   -0.0001
   480        0.7592             nan     0.0100   -0.0000
   500        0.7555             nan     0.0100    0.0001
   520        0.7514             nan     0.0100   -0.0000
   540        0.7479             nan     0.0100   -0.0000
   560        0.7445             nan     0.0100    0.0001
   580        0.7409             nan     0.0100    0.0000
   600        0.7377             nan     0.0100   -0.0000
   620        0.7340             nan     0.0100   -0.0000
   640        0.7312             nan     0.0100   -0.0000
   660        0.7282             nan     0.0100   -0.0002
   680        0.7254             nan     0.0100   -0.0000
   700        0.7226             nan     0.0100   -0.0001
   720        0.7198             nan     0.0100   -0.0001
   740        0.7171             nan     0.0100   -0.0000
   760        0.7147             nan     0.0100   -0.0001
   780        0.7117             nan     0.0100   -0.0001
   800        0.7093             nan     0.0100   -0.0001
   820        0.7073             nan     0.0100   -0.0002
   840        0.7050             nan     0.0100   -0.0001
   860        0.7029             nan     0.0100   -0.0000
   880        0.7009             nan     0.0100   -0.0000
   900        0.6987             nan     0.0100   -0.0000
   920        0.6966             nan     0.0100   -0.0001
   940        0.6943             nan     0.0100   -0.0002
   960        0.6920             nan     0.0100   -0.0000
   980        0.6902             nan     0.0100   -0.0001
  1000        0.6884             nan     0.0100   -0.0001
  1020        0.6867             nan     0.0100   -0.0000
  1040        0.6848             nan     0.0100   -0.0000
  1060        0.6833             nan     0.0100   -0.0002
  1080        0.6812             nan     0.0100   -0.0001
  1100        0.6793             nan     0.0100   -0.0000

- Fold08.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0041
     2        1.3160             nan     0.0100    0.0041
     3        1.3076             nan     0.0100    0.0041
     4        1.2996             nan     0.0100    0.0037
     5        1.2918             nan     0.0100    0.0034
     6        1.2849             nan     0.0100    0.0037
     7        1.2776             nan     0.0100    0.0032
     8        1.2704             nan     0.0100    0.0036
     9        1.2627             nan     0.0100    0.0032
    10        1.2555             nan     0.0100    0.0035
    20        1.1912             nan     0.0100    0.0029
    40        1.0931             nan     0.0100    0.0022
    60        1.0193             nan     0.0100    0.0014
    80        0.9655             nan     0.0100    0.0009
   100        0.9241             nan     0.0100    0.0008
   120        0.8939             nan     0.0100    0.0005
   140        0.8688             nan     0.0100    0.0004
   160        0.8492             nan     0.0100    0.0003
   180        0.8333             nan     0.0100    0.0001
   200        0.8186             nan     0.0100    0.0002
   220        0.8049             nan     0.0100    0.0000
   240        0.7937             nan     0.0100    0.0001
   260        0.7833             nan     0.0100    0.0002
   280        0.7746             nan     0.0100    0.0000
   300        0.7664             nan     0.0100    0.0000
   320        0.7585             nan     0.0100   -0.0000
   340        0.7520             nan     0.0100   -0.0000
   360        0.7455             nan     0.0100   -0.0001
   380        0.7394             nan     0.0100   -0.0000
   400        0.7336             nan     0.0100   -0.0001
   420        0.7281             nan     0.0100   -0.0001
   440        0.7235             nan     0.0100   -0.0000
   460        0.7185             nan     0.0100   -0.0001
   480        0.7147             nan     0.0100   -0.0000
   500        0.7101             nan     0.0100   -0.0001
   520        0.7058             nan     0.0100   -0.0000
   540        0.7013             nan     0.0100   -0.0001
   560        0.6976             nan     0.0100    0.0000
   580        0.6939             nan     0.0100   -0.0001
   600        0.6906             nan     0.0100   -0.0001
   620        0.6865             nan     0.0100   -0.0002
   640        0.6829             nan     0.0100    0.0000
   660        0.6796             nan     0.0100   -0.0000
   680        0.6763             nan     0.0100   -0.0000
   700        0.6726             nan     0.0100    0.0000
   720        0.6695             nan     0.0100   -0.0000
   740        0.6663             nan     0.0100   -0.0001
   760        0.6635             nan     0.0100   -0.0001
   780        0.6605             nan     0.0100    0.0000
   800        0.6576             nan     0.0100   -0.0002
   820        0.6548             nan     0.0100   -0.0002
   840        0.6522             nan     0.0100   -0.0001
   860        0.6494             nan     0.0100   -0.0001
   880        0.6467             nan     0.0100   -0.0001
   900        0.6435             nan     0.0100   -0.0001
   920        0.6409             nan     0.0100   -0.0001
   940        0.6385             nan     0.0100   -0.0002
   960        0.6361             nan     0.0100   -0.0001
   980        0.6336             nan     0.0100   -0.0001
  1000        0.6312             nan     0.0100   -0.0001
  1020        0.6289             nan     0.0100   -0.0001
  1040        0.6267             nan     0.0100   -0.0001
  1060        0.6247             nan     0.0100   -0.0001
  1080        0.6223             nan     0.0100   -0.0001
  1100        0.6200             nan     0.0100   -0.0001

- Fold08.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2761             nan     0.1000    0.0294
     2        1.2276             nan     0.1000    0.0232
     3        1.1875             nan     0.1000    0.0204
     4        1.1550             nan     0.1000    0.0170
     5        1.1289             nan     0.1000    0.0140
     6        1.1114             nan     0.1000    0.0078
     7        1.0904             nan     0.1000    0.0113
     8        1.0698             nan     0.1000    0.0095
     9        1.0543             nan     0.1000    0.0083
    10        1.0394             nan     0.1000    0.0071
    20        0.9376             nan     0.1000    0.0033
    40        0.8591             nan     0.1000    0.0001
    60        0.8187             nan     0.1000   -0.0010
    80        0.7946             nan     0.1000   -0.0003
   100        0.7775             nan     0.1000   -0.0011
   120        0.7636             nan     0.1000   -0.0001
   140        0.7534             nan     0.1000   -0.0004
   160        0.7454             nan     0.1000   -0.0006
   180        0.7346             nan     0.1000   -0.0011
   200        0.7267             nan     0.1000   -0.0012
   220        0.7220             nan     0.1000   -0.0008
   240        0.7159             nan     0.1000   -0.0003
   260        0.7112             nan     0.1000   -0.0002
   280        0.7062             nan     0.1000   -0.0009
   300        0.7013             nan     0.1000   -0.0010
   320        0.6974             nan     0.1000   -0.0010
   340        0.6932             nan     0.1000   -0.0013
   360        0.6893             nan     0.1000   -0.0007
   380        0.6860             nan     0.1000   -0.0013
   400        0.6831             nan     0.1000   -0.0005
   420        0.6804             nan     0.1000   -0.0008
   440        0.6768             nan     0.1000   -0.0009
   460        0.6732             nan     0.1000   -0.0008
   480        0.6718             nan     0.1000   -0.0004
   500        0.6694             nan     0.1000   -0.0007
   520        0.6664             nan     0.1000   -0.0008
   540        0.6643             nan     0.1000   -0.0005
   560        0.6626             nan     0.1000   -0.0007
   580        0.6605             nan     0.1000   -0.0009
   600        0.6575             nan     0.1000   -0.0010
   620        0.6559             nan     0.1000   -0.0011
   640        0.6536             nan     0.1000   -0.0009
   660        0.6502             nan     0.1000   -0.0011
   680        0.6480             nan     0.1000   -0.0008
   700        0.6464             nan     0.1000   -0.0013
   720        0.6448             nan     0.1000   -0.0007
   740        0.6437             nan     0.1000   -0.0004
   760        0.6422             nan     0.1000   -0.0004
   780        0.6404             nan     0.1000   -0.0006
   800        0.6390             nan     0.1000   -0.0007
   820        0.6365             nan     0.1000   -0.0008
   840        0.6339             nan     0.1000   -0.0003
   860        0.6325             nan     0.1000   -0.0005
   880        0.6311             nan     0.1000   -0.0012
   900        0.6299             nan     0.1000   -0.0003
   920        0.6283             nan     0.1000   -0.0010
   940        0.6256             nan     0.1000   -0.0005
   960        0.6252             nan     0.1000   -0.0004
   980        0.6227             nan     0.1000   -0.0006
  1000        0.6221             nan     0.1000   -0.0006
  1020        0.6206             nan     0.1000   -0.0005
  1040        0.6196             nan     0.1000   -0.0005
  1060        0.6186             nan     0.1000   -0.0005
  1080        0.6174             nan     0.1000   -0.0006
  1100        0.6148             nan     0.1000   -0.0005

- Fold08.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2543             nan     0.1000    0.0347
     2        1.1960             nan     0.1000    0.0274
     3        1.1435             nan     0.1000    0.0248
     4        1.1018             nan     0.1000    0.0194
     5        1.0646             nan     0.1000    0.0160
     6        1.0315             nan     0.1000    0.0132
     7        1.0038             nan     0.1000    0.0124
     8        0.9831             nan     0.1000    0.0094
     9        0.9640             nan     0.1000    0.0091
    10        0.9492             nan     0.1000    0.0053
    20        0.8516             nan     0.1000    0.0020
    40        0.7780             nan     0.1000    0.0004
    60        0.7435             nan     0.1000   -0.0004
    80        0.7137             nan     0.1000   -0.0021
   100        0.6890             nan     0.1000   -0.0006
   120        0.6715             nan     0.1000   -0.0007
   140        0.6553             nan     0.1000   -0.0015
   160        0.6426             nan     0.1000   -0.0013
   180        0.6303             nan     0.1000   -0.0007
   200        0.6183             nan     0.1000   -0.0004
   220        0.6066             nan     0.1000   -0.0009
   240        0.5947             nan     0.1000   -0.0007
   260        0.5843             nan     0.1000   -0.0005
   280        0.5765             nan     0.1000   -0.0013
   300        0.5692             nan     0.1000   -0.0006
   320        0.5597             nan     0.1000   -0.0008
   340        0.5516             nan     0.1000   -0.0019
   360        0.5456             nan     0.1000   -0.0012
   380        0.5371             nan     0.1000   -0.0010
   400        0.5294             nan     0.1000   -0.0010
   420        0.5201             nan     0.1000   -0.0009
   440        0.5126             nan     0.1000   -0.0011
   460        0.5076             nan     0.1000   -0.0014
   480        0.5008             nan     0.1000   -0.0010
   500        0.4953             nan     0.1000   -0.0003
   520        0.4890             nan     0.1000   -0.0006
   540        0.4834             nan     0.1000   -0.0008
   560        0.4786             nan     0.1000   -0.0016
   580        0.4747             nan     0.1000   -0.0007
   600        0.4703             nan     0.1000   -0.0006
   620        0.4648             nan     0.1000   -0.0006
   640        0.4611             nan     0.1000   -0.0009
   660        0.4554             nan     0.1000   -0.0003
   680        0.4523             nan     0.1000   -0.0007
   700        0.4486             nan     0.1000   -0.0016
   720        0.4447             nan     0.1000   -0.0004
   740        0.4425             nan     0.1000   -0.0003
   760        0.4376             nan     0.1000   -0.0012
   780        0.4334             nan     0.1000   -0.0006
   800        0.4307             nan     0.1000   -0.0007
   820        0.4265             nan     0.1000   -0.0011
   840        0.4234             nan     0.1000   -0.0015
   860        0.4207             nan     0.1000   -0.0013
   880        0.4159             nan     0.1000   -0.0007
   900        0.4130             nan     0.1000   -0.0013
   920        0.4103             nan     0.1000   -0.0008
   940        0.4052             nan     0.1000   -0.0006
   960        0.4016             nan     0.1000   -0.0004
   980        0.3974             nan     0.1000   -0.0005
  1000        0.3941             nan     0.1000   -0.0014
  1020        0.3909             nan     0.1000   -0.0003
  1040        0.3877             nan     0.1000   -0.0009
  1060        0.3838             nan     0.1000   -0.0012
  1080        0.3814             nan     0.1000   -0.0008
  1100        0.3782             nan     0.1000   -0.0004

- Fold08.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2491             nan     0.1000    0.0373
     2        1.1870             nan     0.1000    0.0285
     3        1.1347             nan     0.1000    0.0256
     4        1.0853             nan     0.1000    0.0210
     5        1.0503             nan     0.1000    0.0187
     6        1.0159             nan     0.1000    0.0173
     7        0.9859             nan     0.1000    0.0146
     8        0.9598             nan     0.1000    0.0113
     9        0.9383             nan     0.1000    0.0073
    10        0.9218             nan     0.1000    0.0081
    20        0.8173             nan     0.1000    0.0015
    40        0.7387             nan     0.1000   -0.0006
    60        0.6924             nan     0.1000   -0.0012
    80        0.6612             nan     0.1000   -0.0014
   100        0.6325             nan     0.1000   -0.0017
   120        0.6108             nan     0.1000   -0.0005
   140        0.5893             nan     0.1000   -0.0027
   160        0.5721             nan     0.1000   -0.0020
   180        0.5555             nan     0.1000   -0.0017
   200        0.5406             nan     0.1000   -0.0017
   220        0.5270             nan     0.1000   -0.0016
   240        0.5153             nan     0.1000   -0.0021
   260        0.5024             nan     0.1000   -0.0006
   280        0.4926             nan     0.1000   -0.0024
   300        0.4818             nan     0.1000   -0.0006
   320        0.4699             nan     0.1000   -0.0005
   340        0.4600             nan     0.1000   -0.0003
   360        0.4522             nan     0.1000   -0.0009
   380        0.4404             nan     0.1000   -0.0002
   400        0.4300             nan     0.1000   -0.0009
   420        0.4234             nan     0.1000   -0.0009
   440        0.4135             nan     0.1000   -0.0008
   460        0.4091             nan     0.1000   -0.0017
   480        0.4014             nan     0.1000   -0.0013
   500        0.3946             nan     0.1000   -0.0010
   520        0.3888             nan     0.1000   -0.0013
   540        0.3836             nan     0.1000   -0.0008
   560        0.3793             nan     0.1000   -0.0007
   580        0.3725             nan     0.1000   -0.0013
   600        0.3664             nan     0.1000   -0.0006
   620        0.3610             nan     0.1000   -0.0016
   640        0.3540             nan     0.1000   -0.0013
   660        0.3497             nan     0.1000   -0.0010
   680        0.3456             nan     0.1000   -0.0008
   700        0.3397             nan     0.1000   -0.0011
   720        0.3358             nan     0.1000   -0.0014
   740        0.3323             nan     0.1000   -0.0015
   760        0.3268             nan     0.1000   -0.0012
   780        0.3213             nan     0.1000   -0.0012
   800        0.3163             nan     0.1000   -0.0017
   820        0.3125             nan     0.1000   -0.0010
   840        0.3087             nan     0.1000   -0.0014
   860        0.3041             nan     0.1000   -0.0009
   880        0.3000             nan     0.1000   -0.0005
   900        0.2949             nan     0.1000   -0.0013
   920        0.2923             nan     0.1000   -0.0012
   940        0.2868             nan     0.1000   -0.0014
   960        0.2834             nan     0.1000   -0.0006
   980        0.2790             nan     0.1000   -0.0005
  1000        0.2752             nan     0.1000   -0.0009
  1020        0.2725             nan     0.1000   -0.0009
  1040        0.2700             nan     0.1000   -0.0008
  1060        0.2663             nan     0.1000   -0.0009
  1080        0.2641             nan     0.1000   -0.0010
  1100        0.2601             nan     0.1000   -0.0012

- Fold08.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3269             nan     0.0100    0.0028
     2        1.3206             nan     0.0100    0.0029
     3        1.3144             nan     0.0100    0.0029
     4        1.3085             nan     0.0100    0.0028
     5        1.3026             nan     0.0100    0.0027
     6        1.2968             nan     0.0100    0.0026
     7        1.2916             nan     0.0100    0.0026
     8        1.2866             nan     0.0100    0.0025
     9        1.2813             nan     0.0100    0.0025
    10        1.2760             nan     0.0100    0.0025
    20        1.2321             nan     0.0100    0.0019
    40        1.1649             nan     0.0100    0.0014
    60        1.1177             nan     0.0100    0.0010
    80        1.0824             nan     0.0100    0.0007
   100        1.0539             nan     0.0100    0.0006
   120        1.0301             nan     0.0100    0.0004
   140        1.0107             nan     0.0100    0.0004
   160        0.9941             nan     0.0100    0.0003
   180        0.9796             nan     0.0100    0.0003
   200        0.9671             nan     0.0100    0.0001
   220        0.9563             nan     0.0100    0.0002
   240        0.9458             nan     0.0100    0.0002
   260        0.9363             nan     0.0100    0.0002
   280        0.9277             nan     0.0100    0.0001
   300        0.9204             nan     0.0100    0.0001
   320        0.9138             nan     0.0100    0.0001
   340        0.9077             nan     0.0100    0.0001
   360        0.9015             nan     0.0100    0.0001
   380        0.8963             nan     0.0100    0.0001
   400        0.8915             nan     0.0100    0.0000
   420        0.8868             nan     0.0100    0.0001
   440        0.8821             nan     0.0100    0.0000
   460        0.8779             nan     0.0100    0.0000
   480        0.8738             nan     0.0100    0.0000
   500        0.8699             nan     0.0100   -0.0000
   520        0.8663             nan     0.0100    0.0000
   540        0.8627             nan     0.0100    0.0000
   560        0.8594             nan     0.0100    0.0000
   580        0.8562             nan     0.0100    0.0000
   600        0.8535             nan     0.0100   -0.0000
   620        0.8509             nan     0.0100   -0.0000
   640        0.8480             nan     0.0100    0.0000
   660        0.8452             nan     0.0100   -0.0000
   680        0.8426             nan     0.0100   -0.0001
   700        0.8403             nan     0.0100   -0.0001
   720        0.8378             nan     0.0100   -0.0000
   740        0.8355             nan     0.0100   -0.0000
   760        0.8332             nan     0.0100    0.0000
   780        0.8310             nan     0.0100   -0.0001
   800        0.8291             nan     0.0100   -0.0000
   820        0.8269             nan     0.0100   -0.0000
   840        0.8248             nan     0.0100    0.0000
   860        0.8227             nan     0.0100   -0.0000
   880        0.8208             nan     0.0100   -0.0000
   900        0.8191             nan     0.0100   -0.0000
   920        0.8174             nan     0.0100   -0.0001
   940        0.8159             nan     0.0100   -0.0000
   960        0.8143             nan     0.0100   -0.0001
   980        0.8127             nan     0.0100   -0.0001
  1000        0.8112             nan     0.0100   -0.0001
  1020        0.8096             nan     0.0100   -0.0001
  1040        0.8081             nan     0.0100   -0.0000
  1060        0.8068             nan     0.0100    0.0000
  1080        0.8054             nan     0.0100   -0.0001
  1100        0.8041             nan     0.0100   -0.0000

- Fold09.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0034
     2        1.3172             nan     0.0100    0.0033
     3        1.3097             nan     0.0100    0.0035
     4        1.3025             nan     0.0100    0.0033
     5        1.2960             nan     0.0100    0.0033
     6        1.2892             nan     0.0100    0.0032
     7        1.2828             nan     0.0100    0.0028
     8        1.2763             nan     0.0100    0.0031
     9        1.2709             nan     0.0100    0.0027
    10        1.2646             nan     0.0100    0.0030
    20        1.2100             nan     0.0100    0.0025
    40        1.1249             nan     0.0100    0.0018
    60        1.0621             nan     0.0100    0.0013
    80        1.0159             nan     0.0100    0.0009
   100        0.9812             nan     0.0100    0.0006
   120        0.9547             nan     0.0100    0.0004
   140        0.9334             nan     0.0100    0.0004
   160        0.9158             nan     0.0100    0.0003
   180        0.9011             nan     0.0100    0.0001
   200        0.8882             nan     0.0100    0.0002
   220        0.8774             nan     0.0100    0.0002
   240        0.8672             nan     0.0100    0.0001
   260        0.8581             nan     0.0100    0.0001
   280        0.8491             nan     0.0100    0.0001
   300        0.8409             nan     0.0100    0.0001
   320        0.8341             nan     0.0100    0.0001
   340        0.8271             nan     0.0100    0.0002
   360        0.8207             nan     0.0100    0.0000
   380        0.8150             nan     0.0100   -0.0000
   400        0.8093             nan     0.0100   -0.0000
   420        0.8043             nan     0.0100   -0.0000
   440        0.7995             nan     0.0100   -0.0001
   460        0.7948             nan     0.0100    0.0001
   480        0.7909             nan     0.0100   -0.0002
   500        0.7867             nan     0.0100   -0.0000
   520        0.7833             nan     0.0100   -0.0001
   540        0.7792             nan     0.0100    0.0000
   560        0.7757             nan     0.0100   -0.0002
   580        0.7722             nan     0.0100   -0.0001
   600        0.7690             nan     0.0100   -0.0000
   620        0.7655             nan     0.0100   -0.0001
   640        0.7623             nan     0.0100   -0.0001
   660        0.7595             nan     0.0100   -0.0001
   680        0.7564             nan     0.0100   -0.0001
   700        0.7541             nan     0.0100   -0.0001
   720        0.7518             nan     0.0100   -0.0001
   740        0.7494             nan     0.0100   -0.0000
   760        0.7469             nan     0.0100   -0.0001
   780        0.7445             nan     0.0100   -0.0000
   800        0.7424             nan     0.0100   -0.0000
   820        0.7404             nan     0.0100   -0.0000
   840        0.7380             nan     0.0100    0.0000
   860        0.7359             nan     0.0100   -0.0001
   880        0.7342             nan     0.0100   -0.0001
   900        0.7322             nan     0.0100   -0.0000
   920        0.7299             nan     0.0100   -0.0000
   940        0.7275             nan     0.0100   -0.0000
   960        0.7253             nan     0.0100   -0.0001
   980        0.7234             nan     0.0100   -0.0000
  1000        0.7217             nan     0.0100   -0.0002
  1020        0.7199             nan     0.0100   -0.0001
  1040        0.7180             nan     0.0100   -0.0001
  1060        0.7161             nan     0.0100   -0.0000
  1080        0.7147             nan     0.0100   -0.0000
  1100        0.7125             nan     0.0100   -0.0000

- Fold09.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3236             nan     0.0100    0.0038
     2        1.3158             nan     0.0100    0.0038
     3        1.3079             nan     0.0100    0.0039
     4        1.3010             nan     0.0100    0.0036
     5        1.2935             nan     0.0100    0.0035
     6        1.2863             nan     0.0100    0.0036
     7        1.2791             nan     0.0100    0.0033
     8        1.2723             nan     0.0100    0.0033
     9        1.2653             nan     0.0100    0.0033
    10        1.2591             nan     0.0100    0.0032
    20        1.1987             nan     0.0100    0.0027
    40        1.1039             nan     0.0100    0.0019
    60        1.0362             nan     0.0100    0.0014
    80        0.9867             nan     0.0100    0.0010
   100        0.9474             nan     0.0100    0.0006
   120        0.9176             nan     0.0100    0.0005
   140        0.8925             nan     0.0100    0.0004
   160        0.8732             nan     0.0100    0.0003
   180        0.8577             nan     0.0100    0.0003
   200        0.8446             nan     0.0100    0.0001
   220        0.8327             nan     0.0100    0.0001
   240        0.8221             nan     0.0100    0.0001
   260        0.8131             nan     0.0100   -0.0000
   280        0.8040             nan     0.0100    0.0001
   300        0.7962             nan     0.0100   -0.0001
   320        0.7893             nan     0.0100   -0.0001
   340        0.7827             nan     0.0100   -0.0001
   360        0.7765             nan     0.0100   -0.0001
   380        0.7702             nan     0.0100   -0.0000
   400        0.7649             nan     0.0100   -0.0001
   420        0.7602             nan     0.0100    0.0000
   440        0.7550             nan     0.0100   -0.0001
   460        0.7503             nan     0.0100   -0.0000
   480        0.7461             nan     0.0100    0.0000
   500        0.7417             nan     0.0100    0.0001
   520        0.7378             nan     0.0100   -0.0001
   540        0.7338             nan     0.0100   -0.0001
   560        0.7301             nan     0.0100   -0.0001
   580        0.7268             nan     0.0100   -0.0001
   600        0.7229             nan     0.0100   -0.0001
   620        0.7191             nan     0.0100   -0.0001
   640        0.7147             nan     0.0100   -0.0001
   660        0.7110             nan     0.0100   -0.0001
   680        0.7076             nan     0.0100   -0.0001
   700        0.7046             nan     0.0100   -0.0002
   720        0.7012             nan     0.0100   -0.0001
   740        0.6984             nan     0.0100   -0.0002
   760        0.6957             nan     0.0100   -0.0002
   780        0.6928             nan     0.0100   -0.0000
   800        0.6895             nan     0.0100   -0.0000
   820        0.6867             nan     0.0100   -0.0001
   840        0.6837             nan     0.0100   -0.0000
   860        0.6811             nan     0.0100   -0.0001
   880        0.6783             nan     0.0100   -0.0001
   900        0.6758             nan     0.0100    0.0000
   920        0.6731             nan     0.0100   -0.0002
   940        0.6703             nan     0.0100   -0.0001
   960        0.6680             nan     0.0100   -0.0000
   980        0.6653             nan     0.0100   -0.0000
  1000        0.6627             nan     0.0100   -0.0001
  1020        0.6604             nan     0.0100   -0.0000
  1040        0.6575             nan     0.0100   -0.0001
  1060        0.6547             nan     0.0100   -0.0002
  1080        0.6525             nan     0.0100   -0.0001
  1100        0.6502             nan     0.0100   -0.0002

- Fold09.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2756             nan     0.1000    0.0281
     2        1.2300             nan     0.1000    0.0228
     3        1.1925             nan     0.1000    0.0177
     4        1.1652             nan     0.1000    0.0149
     5        1.1397             nan     0.1000    0.0133
     6        1.1184             nan     0.1000    0.0110
     7        1.1011             nan     0.1000    0.0065
     8        1.0831             nan     0.1000    0.0088
     9        1.0657             nan     0.1000    0.0072
    10        1.0513             nan     0.1000    0.0057
    20        0.9664             nan     0.1000    0.0029
    40        0.8901             nan     0.1000    0.0008
    60        0.8533             nan     0.1000    0.0004
    80        0.8304             nan     0.1000   -0.0009
   100        0.8132             nan     0.1000   -0.0004
   120        0.7998             nan     0.1000   -0.0012
   140        0.7881             nan     0.1000   -0.0008
   160        0.7806             nan     0.1000   -0.0004
   180        0.7722             nan     0.1000   -0.0005
   200        0.7681             nan     0.1000   -0.0005
   220        0.7599             nan     0.1000   -0.0008
   240        0.7543             nan     0.1000   -0.0006
   260        0.7496             nan     0.1000   -0.0005
   280        0.7449             nan     0.1000   -0.0007
   300        0.7403             nan     0.1000   -0.0003
   320        0.7379             nan     0.1000   -0.0006
   340        0.7333             nan     0.1000   -0.0008
   360        0.7281             nan     0.1000   -0.0009
   380        0.7247             nan     0.1000   -0.0012
   400        0.7231             nan     0.1000   -0.0010
   420        0.7191             nan     0.1000   -0.0006
   440        0.7165             nan     0.1000   -0.0004
   460        0.7139             nan     0.1000   -0.0010
   480        0.7111             nan     0.1000   -0.0009
   500        0.7092             nan     0.1000   -0.0011
   520        0.7069             nan     0.1000   -0.0016
   540        0.7051             nan     0.1000   -0.0004
   560        0.7016             nan     0.1000   -0.0005
   580        0.6988             nan     0.1000   -0.0006
   600        0.6971             nan     0.1000   -0.0007
   620        0.6956             nan     0.1000   -0.0010
   640        0.6946             nan     0.1000   -0.0005
   660        0.6921             nan     0.1000   -0.0006
   680        0.6911             nan     0.1000   -0.0006
   700        0.6886             nan     0.1000   -0.0004
   720        0.6865             nan     0.1000   -0.0012
   740        0.6855             nan     0.1000   -0.0010
   760        0.6833             nan     0.1000   -0.0016
   780        0.6824             nan     0.1000   -0.0008
   800        0.6799             nan     0.1000   -0.0007
   820        0.6788             nan     0.1000   -0.0005
   840        0.6767             nan     0.1000   -0.0007
   860        0.6747             nan     0.1000   -0.0007
   880        0.6731             nan     0.1000   -0.0006
   900        0.6720             nan     0.1000   -0.0002
   920        0.6714             nan     0.1000   -0.0002
   940        0.6688             nan     0.1000   -0.0005
   960        0.6667             nan     0.1000   -0.0007
   980        0.6658             nan     0.1000   -0.0005
  1000        0.6641             nan     0.1000   -0.0011
  1020        0.6623             nan     0.1000   -0.0006
  1040        0.6624             nan     0.1000   -0.0010
  1060        0.6606             nan     0.1000   -0.0011
  1080        0.6596             nan     0.1000   -0.0010
  1100        0.6578             nan     0.1000   -0.0007

- Fold09.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2637             nan     0.1000    0.0339
     2        1.2079             nan     0.1000    0.0273
     3        1.1624             nan     0.1000    0.0222
     4        1.1235             nan     0.1000    0.0203
     5        1.0902             nan     0.1000    0.0164
     6        1.0573             nan     0.1000    0.0143
     7        1.0327             nan     0.1000    0.0114
     8        1.0105             nan     0.1000    0.0097
     9        0.9927             nan     0.1000    0.0082
    10        0.9795             nan     0.1000    0.0050
    20        0.8816             nan     0.1000    0.0017
    40        0.8087             nan     0.1000   -0.0003
    60        0.7690             nan     0.1000   -0.0011
    80        0.7451             nan     0.1000   -0.0012
   100        0.7243             nan     0.1000   -0.0011
   120        0.7070             nan     0.1000   -0.0003
   140        0.6926             nan     0.1000   -0.0014
   160        0.6737             nan     0.1000   -0.0009
   180        0.6624             nan     0.1000   -0.0010
   200        0.6532             nan     0.1000   -0.0015
   220        0.6411             nan     0.1000   -0.0010
   240        0.6293             nan     0.1000   -0.0003
   260        0.6176             nan     0.1000   -0.0012
   280        0.6098             nan     0.1000   -0.0006
   300        0.6031             nan     0.1000   -0.0006
   320        0.5938             nan     0.1000   -0.0009
   340        0.5872             nan     0.1000   -0.0010
   360        0.5812             nan     0.1000   -0.0006
   380        0.5741             nan     0.1000   -0.0010
   400        0.5667             nan     0.1000   -0.0011
   420        0.5595             nan     0.1000   -0.0010
   440        0.5527             nan     0.1000   -0.0009
   460        0.5444             nan     0.1000   -0.0009
   480        0.5372             nan     0.1000   -0.0009
   500        0.5309             nan     0.1000   -0.0009
   520        0.5252             nan     0.1000   -0.0013
   540        0.5204             nan     0.1000   -0.0017
   560        0.5139             nan     0.1000   -0.0008
   580        0.5071             nan     0.1000   -0.0010
   600        0.5026             nan     0.1000   -0.0011
   620        0.4980             nan     0.1000   -0.0008
   640        0.4940             nan     0.1000   -0.0008
   660        0.4914             nan     0.1000   -0.0005
   680        0.4854             nan     0.1000   -0.0007
   700        0.4794             nan     0.1000   -0.0008
   720        0.4727             nan     0.1000   -0.0006
   740        0.4677             nan     0.1000   -0.0011
   760        0.4640             nan     0.1000   -0.0005
   780        0.4606             nan     0.1000   -0.0010
   800        0.4564             nan     0.1000   -0.0014
   820        0.4530             nan     0.1000   -0.0005
   840        0.4487             nan     0.1000   -0.0006
   860        0.4441             nan     0.1000   -0.0005
   880        0.4416             nan     0.1000   -0.0012
   900        0.4368             nan     0.1000   -0.0008
   920        0.4339             nan     0.1000   -0.0013
   940        0.4304             nan     0.1000   -0.0007
   960        0.4264             nan     0.1000   -0.0005
   980        0.4219             nan     0.1000   -0.0008
  1000        0.4188             nan     0.1000   -0.0012
  1020        0.4147             nan     0.1000   -0.0005
  1040        0.4121             nan     0.1000   -0.0008
  1060        0.4093             nan     0.1000   -0.0006
  1080        0.4064             nan     0.1000   -0.0010
  1100        0.4041             nan     0.1000   -0.0010

- Fold09.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2582             nan     0.1000    0.0348
     2        1.1946             nan     0.1000    0.0316
     3        1.1389             nan     0.1000    0.0250
     4        1.0964             nan     0.1000    0.0209
     5        1.0651             nan     0.1000    0.0172
     6        1.0350             nan     0.1000    0.0153
     7        1.0051             nan     0.1000    0.0143
     8        0.9834             nan     0.1000    0.0092
     9        0.9642             nan     0.1000    0.0074
    10        0.9475             nan     0.1000    0.0057
    20        0.8479             nan     0.1000    0.0014
    40        0.7631             nan     0.1000   -0.0005
    60        0.7216             nan     0.1000   -0.0000
    80        0.6896             nan     0.1000   -0.0008
   100        0.6646             nan     0.1000   -0.0024
   120        0.6419             nan     0.1000   -0.0001
   140        0.6266             nan     0.1000   -0.0022
   160        0.6066             nan     0.1000   -0.0008
   180        0.5937             nan     0.1000   -0.0012
   200        0.5812             nan     0.1000   -0.0021
   220        0.5682             nan     0.1000   -0.0018
   240        0.5564             nan     0.1000   -0.0009
   260        0.5447             nan     0.1000   -0.0022
   280        0.5302             nan     0.1000   -0.0013
   300        0.5185             nan     0.1000   -0.0008
   320        0.5081             nan     0.1000   -0.0006
   340        0.4990             nan     0.1000   -0.0015
   360        0.4889             nan     0.1000   -0.0014
   380        0.4770             nan     0.1000   -0.0011
   400        0.4654             nan     0.1000   -0.0005
   420        0.4557             nan     0.1000   -0.0011
   440        0.4449             nan     0.1000   -0.0003
   460        0.4383             nan     0.1000   -0.0007
   480        0.4314             nan     0.1000   -0.0014
   500        0.4232             nan     0.1000   -0.0016
   520        0.4165             nan     0.1000   -0.0010
   540        0.4075             nan     0.1000   -0.0009
   560        0.3998             nan     0.1000   -0.0007
   580        0.3940             nan     0.1000   -0.0012
   600        0.3883             nan     0.1000   -0.0017
   620        0.3836             nan     0.1000   -0.0014
   640        0.3770             nan     0.1000   -0.0014
   660        0.3710             nan     0.1000   -0.0008
   680        0.3652             nan     0.1000   -0.0020
   700        0.3597             nan     0.1000   -0.0019
   720        0.3522             nan     0.1000   -0.0012
   740        0.3468             nan     0.1000   -0.0007
   760        0.3424             nan     0.1000   -0.0018
   780        0.3388             nan     0.1000   -0.0007
   800        0.3331             nan     0.1000   -0.0013
   820        0.3283             nan     0.1000   -0.0006
   840        0.3245             nan     0.1000   -0.0008
   860        0.3185             nan     0.1000   -0.0005
   880        0.3129             nan     0.1000   -0.0007
   900        0.3090             nan     0.1000   -0.0011
   920        0.3052             nan     0.1000   -0.0012
   940        0.3016             nan     0.1000   -0.0010
   960        0.2973             nan     0.1000   -0.0014
   980        0.2943             nan     0.1000   -0.0012
  1000        0.2919             nan     0.1000   -0.0009
  1020        0.2874             nan     0.1000   -0.0008
  1040        0.2850             nan     0.1000   -0.0009
  1060        0.2801             nan     0.1000   -0.0006
  1080        0.2775             nan     0.1000   -0.0009
  1100        0.2734             nan     0.1000   -0.0008

- Fold09.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3255             nan     0.0100    0.0030
     2        1.3196             nan     0.0100    0.0029
     3        1.3140             nan     0.0100    0.0029
     4        1.3085             nan     0.0100    0.0028
     5        1.3021             nan     0.0100    0.0027
     6        1.2962             nan     0.0100    0.0028
     7        1.2907             nan     0.0100    0.0024
     8        1.2851             nan     0.0100    0.0026
     9        1.2796             nan     0.0100    0.0024
    10        1.2748             nan     0.0100    0.0025
    20        1.2304             nan     0.0100    0.0021
    40        1.1623             nan     0.0100    0.0014
    60        1.1128             nan     0.0100    0.0009
    80        1.0735             nan     0.0100    0.0008
   100        1.0423             nan     0.0100    0.0007
   120        1.0169             nan     0.0100    0.0004
   140        0.9944             nan     0.0100    0.0005
   160        0.9759             nan     0.0100    0.0004
   180        0.9611             nan     0.0100    0.0003
   200        0.9478             nan     0.0100    0.0003
   220        0.9358             nan     0.0100    0.0002
   240        0.9247             nan     0.0100    0.0001
   260        0.9155             nan     0.0100    0.0002
   280        0.9066             nan     0.0100    0.0001
   300        0.8986             nan     0.0100    0.0002
   320        0.8916             nan     0.0100    0.0001
   340        0.8847             nan     0.0100    0.0001
   360        0.8788             nan     0.0100   -0.0001
   380        0.8733             nan     0.0100    0.0001
   400        0.8678             nan     0.0100    0.0000
   420        0.8629             nan     0.0100    0.0000
   440        0.8588             nan     0.0100    0.0000
   460        0.8543             nan     0.0100    0.0001
   480        0.8501             nan     0.0100    0.0000
   500        0.8463             nan     0.0100    0.0000
   520        0.8423             nan     0.0100    0.0000
   540        0.8387             nan     0.0100   -0.0000
   560        0.8353             nan     0.0100    0.0000
   580        0.8320             nan     0.0100    0.0000
   600        0.8288             nan     0.0100   -0.0000
   620        0.8256             nan     0.0100   -0.0000
   640        0.8224             nan     0.0100    0.0000
   660        0.8198             nan     0.0100   -0.0000
   680        0.8169             nan     0.0100    0.0000
   700        0.8144             nan     0.0100   -0.0000
   720        0.8118             nan     0.0100   -0.0001
   740        0.8094             nan     0.0100    0.0000
   760        0.8069             nan     0.0100   -0.0001
   780        0.8045             nan     0.0100   -0.0001
   800        0.8025             nan     0.0100   -0.0001
   820        0.8004             nan     0.0100    0.0000
   840        0.7983             nan     0.0100    0.0000
   860        0.7961             nan     0.0100   -0.0000
   880        0.7941             nan     0.0100   -0.0001
   900        0.7925             nan     0.0100   -0.0000
   920        0.7908             nan     0.0100   -0.0001
   940        0.7892             nan     0.0100   -0.0000
   960        0.7875             nan     0.0100    0.0000
   980        0.7857             nan     0.0100   -0.0000
  1000        0.7839             nan     0.0100   -0.0000
  1020        0.7822             nan     0.0100   -0.0001
  1040        0.7807             nan     0.0100    0.0000
  1060        0.7794             nan     0.0100   -0.0001
  1080        0.7780             nan     0.0100   -0.0000
  1100        0.7764             nan     0.0100   -0.0000

- Fold10.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3237             nan     0.0100    0.0037
     2        1.3163             nan     0.0100    0.0038
     3        1.3086             nan     0.0100    0.0033
     4        1.3016             nan     0.0100    0.0034
     5        1.2947             nan     0.0100    0.0034
     6        1.2883             nan     0.0100    0.0032
     7        1.2821             nan     0.0100    0.0032
     8        1.2758             nan     0.0100    0.0029
     9        1.2694             nan     0.0100    0.0031
    10        1.2632             nan     0.0100    0.0031
    20        1.2066             nan     0.0100    0.0024
    40        1.1199             nan     0.0100    0.0019
    60        1.0548             nan     0.0100    0.0014
    80        1.0045             nan     0.0100    0.0010
   100        0.9664             nan     0.0100    0.0007
   120        0.9379             nan     0.0100    0.0004
   140        0.9144             nan     0.0100    0.0003
   160        0.8944             nan     0.0100    0.0003
   180        0.8779             nan     0.0100    0.0001
   200        0.8635             nan     0.0100    0.0001
   220        0.8515             nan     0.0100    0.0003
   240        0.8405             nan     0.0100    0.0001
   260        0.8311             nan     0.0100    0.0001
   280        0.8228             nan     0.0100    0.0001
   300        0.8151             nan     0.0100    0.0002
   320        0.8078             nan     0.0100    0.0001
   340        0.8018             nan     0.0100   -0.0000
   360        0.7953             nan     0.0100    0.0000
   380        0.7893             nan     0.0100   -0.0001
   400        0.7839             nan     0.0100    0.0000
   420        0.7794             nan     0.0100    0.0000
   440        0.7749             nan     0.0100    0.0000
   460        0.7708             nan     0.0100    0.0000
   480        0.7668             nan     0.0100    0.0000
   500        0.7623             nan     0.0100    0.0000
   520        0.7586             nan     0.0100   -0.0000
   540        0.7555             nan     0.0100   -0.0001
   560        0.7520             nan     0.0100   -0.0001
   580        0.7487             nan     0.0100   -0.0001
   600        0.7453             nan     0.0100   -0.0001
   620        0.7423             nan     0.0100   -0.0000
   640        0.7395             nan     0.0100   -0.0001
   660        0.7367             nan     0.0100   -0.0000
   680        0.7338             nan     0.0100   -0.0000
   700        0.7307             nan     0.0100   -0.0000
   720        0.7283             nan     0.0100   -0.0000
   740        0.7260             nan     0.0100   -0.0001
   760        0.7235             nan     0.0100    0.0000
   780        0.7210             nan     0.0100   -0.0000
   800        0.7189             nan     0.0100   -0.0001
   820        0.7161             nan     0.0100   -0.0001
   840        0.7139             nan     0.0100   -0.0000
   860        0.7115             nan     0.0100   -0.0001
   880        0.7093             nan     0.0100   -0.0001
   900        0.7070             nan     0.0100   -0.0000
   920        0.7052             nan     0.0100   -0.0001
   940        0.7032             nan     0.0100   -0.0001
   960        0.7014             nan     0.0100   -0.0002
   980        0.6991             nan     0.0100   -0.0000
  1000        0.6970             nan     0.0100   -0.0001
  1020        0.6950             nan     0.0100   -0.0001
  1040        0.6932             nan     0.0100   -0.0001
  1060        0.6914             nan     0.0100   -0.0001
  1080        0.6895             nan     0.0100   -0.0000
  1100        0.6873             nan     0.0100   -0.0000

- Fold10.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3228             nan     0.0100    0.0039
     2        1.3151             nan     0.0100    0.0037
     3        1.3071             nan     0.0100    0.0036
     4        1.2993             nan     0.0100    0.0038
     5        1.2918             nan     0.0100    0.0038
     6        1.2845             nan     0.0100    0.0036
     7        1.2767             nan     0.0100    0.0037
     8        1.2693             nan     0.0100    0.0036
     9        1.2621             nan     0.0100    0.0032
    10        1.2553             nan     0.0100    0.0033
    20        1.1941             nan     0.0100    0.0026
    40        1.0964             nan     0.0100    0.0018
    60        1.0256             nan     0.0100    0.0015
    80        0.9721             nan     0.0100    0.0010
   100        0.9312             nan     0.0100    0.0008
   120        0.9004             nan     0.0100    0.0003
   140        0.8752             nan     0.0100    0.0003
   160        0.8552             nan     0.0100    0.0003
   180        0.8376             nan     0.0100    0.0001
   200        0.8227             nan     0.0100    0.0002
   220        0.8098             nan     0.0100    0.0000
   240        0.7987             nan     0.0100    0.0000
   260        0.7895             nan     0.0100   -0.0001
   280        0.7807             nan     0.0100    0.0001
   300        0.7728             nan     0.0100    0.0001
   320        0.7645             nan     0.0100    0.0000
   340        0.7572             nan     0.0100    0.0001
   360        0.7507             nan     0.0100    0.0000
   380        0.7445             nan     0.0100    0.0000
   400        0.7385             nan     0.0100   -0.0000
   420        0.7332             nan     0.0100   -0.0002
   440        0.7283             nan     0.0100   -0.0002
   460        0.7230             nan     0.0100   -0.0001
   480        0.7186             nan     0.0100    0.0000
   500        0.7141             nan     0.0100   -0.0001
   520        0.7100             nan     0.0100   -0.0001
   540        0.7058             nan     0.0100   -0.0001
   560        0.7014             nan     0.0100   -0.0002
   580        0.6977             nan     0.0100   -0.0002
   600        0.6936             nan     0.0100   -0.0001
   620        0.6899             nan     0.0100   -0.0001
   640        0.6868             nan     0.0100   -0.0003
   660        0.6834             nan     0.0100   -0.0001
   680        0.6800             nan     0.0100   -0.0000
   700        0.6770             nan     0.0100   -0.0001
   720        0.6743             nan     0.0100   -0.0000
   740        0.6714             nan     0.0100   -0.0001
   760        0.6685             nan     0.0100   -0.0001
   780        0.6655             nan     0.0100   -0.0001
   800        0.6626             nan     0.0100   -0.0000
   820        0.6602             nan     0.0100   -0.0001
   840        0.6576             nan     0.0100   -0.0001
   860        0.6555             nan     0.0100   -0.0002
   880        0.6526             nan     0.0100   -0.0000
   900        0.6494             nan     0.0100   -0.0001
   920        0.6471             nan     0.0100   -0.0000
   940        0.6443             nan     0.0100   -0.0001
   960        0.6414             nan     0.0100   -0.0001
   980        0.6389             nan     0.0100   -0.0001
  1000        0.6367             nan     0.0100   -0.0002
  1020        0.6341             nan     0.0100   -0.0000
  1040        0.6314             nan     0.0100   -0.0001
  1060        0.6292             nan     0.0100   -0.0002
  1080        0.6266             nan     0.0100   -0.0002
  1100        0.6243             nan     0.0100   -0.0001

- Fold10.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2716             nan     0.1000    0.0281
     2        1.2294             nan     0.1000    0.0219
     3        1.1935             nan     0.1000    0.0196
     4        1.1601             nan     0.1000    0.0165
     5        1.1395             nan     0.1000    0.0089
     6        1.1142             nan     0.1000    0.0113
     7        1.0904             nan     0.1000    0.0108
     8        1.0721             nan     0.1000    0.0092
     9        1.0563             nan     0.1000    0.0072
    10        1.0415             nan     0.1000    0.0069
    20        0.9500             nan     0.1000   -0.0001
    40        0.8681             nan     0.1000    0.0007
    60        0.8285             nan     0.1000    0.0002
    80        0.8029             nan     0.1000    0.0001
   100        0.7844             nan     0.1000   -0.0004
   120        0.7710             nan     0.1000   -0.0001
   140        0.7578             nan     0.1000   -0.0005
   160        0.7483             nan     0.1000   -0.0003
   180        0.7407             nan     0.1000   -0.0012
   200        0.7331             nan     0.1000   -0.0002
   220        0.7280             nan     0.1000   -0.0017
   240        0.7210             nan     0.1000   -0.0004
   260        0.7150             nan     0.1000   -0.0016
   280        0.7108             nan     0.1000   -0.0007
   300        0.7074             nan     0.1000   -0.0004
   320        0.7043             nan     0.1000   -0.0006
   340        0.6986             nan     0.1000   -0.0005
   360        0.6965             nan     0.1000   -0.0003
   380        0.6932             nan     0.1000   -0.0010
   400        0.6903             nan     0.1000   -0.0006
   420        0.6874             nan     0.1000   -0.0007
   440        0.6857             nan     0.1000   -0.0009
   460        0.6824             nan     0.1000   -0.0006
   480        0.6798             nan     0.1000   -0.0004
   500        0.6777             nan     0.1000   -0.0005
   520        0.6760             nan     0.1000   -0.0011
   540        0.6737             nan     0.1000   -0.0014
   560        0.6712             nan     0.1000   -0.0002
   580        0.6691             nan     0.1000   -0.0013
   600        0.6670             nan     0.1000   -0.0008
   620        0.6661             nan     0.1000   -0.0005
   640        0.6630             nan     0.1000   -0.0009
   660        0.6606             nan     0.1000   -0.0013
   680        0.6580             nan     0.1000   -0.0002
   700        0.6564             nan     0.1000   -0.0009
   720        0.6528             nan     0.1000   -0.0006
   740        0.6525             nan     0.1000   -0.0008
   760        0.6505             nan     0.1000   -0.0003
   780        0.6488             nan     0.1000   -0.0008
   800        0.6468             nan     0.1000   -0.0008
   820        0.6455             nan     0.1000   -0.0007
   840        0.6448             nan     0.1000   -0.0008
   860        0.6425             nan     0.1000   -0.0004
   880        0.6416             nan     0.1000   -0.0008
   900        0.6396             nan     0.1000   -0.0005
   920        0.6375             nan     0.1000   -0.0005
   940        0.6365             nan     0.1000   -0.0008
   960        0.6352             nan     0.1000   -0.0007
   980        0.6334             nan     0.1000   -0.0003
  1000        0.6314             nan     0.1000   -0.0016
  1020        0.6300             nan     0.1000   -0.0005
  1040        0.6288             nan     0.1000   -0.0007
  1060        0.6278             nan     0.1000   -0.0006
  1080        0.6262             nan     0.1000   -0.0010
  1100        0.6252             nan     0.1000   -0.0004

- Fold10.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2598             nan     0.1000    0.0333
     2        1.2017             nan     0.1000    0.0279
     3        1.1512             nan     0.1000    0.0219
     4        1.1105             nan     0.1000    0.0186
     5        1.0789             nan     0.1000    0.0141
     6        1.0488             nan     0.1000    0.0136
     7        1.0253             nan     0.1000    0.0125
     8        1.0058             nan     0.1000    0.0106
     9        0.9819             nan     0.1000    0.0106
    10        0.9648             nan     0.1000    0.0073
    20        0.8646             nan     0.1000    0.0029
    40        0.7906             nan     0.1000   -0.0014
    60        0.7509             nan     0.1000   -0.0001
    80        0.7198             nan     0.1000   -0.0013
   100        0.6989             nan     0.1000   -0.0005
   120        0.6802             nan     0.1000   -0.0003
   140        0.6670             nan     0.1000   -0.0007
   160        0.6513             nan     0.1000   -0.0012
   180        0.6354             nan     0.1000   -0.0017
   200        0.6252             nan     0.1000   -0.0011
   220        0.6124             nan     0.1000   -0.0012
   240        0.5983             nan     0.1000   -0.0012
   260        0.5892             nan     0.1000   -0.0002
   280        0.5807             nan     0.1000   -0.0012
   300        0.5746             nan     0.1000   -0.0015
   320        0.5673             nan     0.1000   -0.0011
   340        0.5615             nan     0.1000   -0.0010
   360        0.5538             nan     0.1000   -0.0011
   380        0.5475             nan     0.1000   -0.0010
   400        0.5398             nan     0.1000   -0.0012
   420        0.5338             nan     0.1000   -0.0008
   440        0.5279             nan     0.1000   -0.0012
   460        0.5214             nan     0.1000   -0.0004
   480        0.5166             nan     0.1000   -0.0010
   500        0.5092             nan     0.1000   -0.0007
   520        0.5027             nan     0.1000   -0.0008
   540        0.4985             nan     0.1000   -0.0008
   560        0.4933             nan     0.1000   -0.0007
   580        0.4889             nan     0.1000   -0.0005
   600        0.4862             nan     0.1000   -0.0013
   620        0.4788             nan     0.1000   -0.0007
   640        0.4745             nan     0.1000   -0.0013
   660        0.4712             nan     0.1000   -0.0011
   680        0.4656             nan     0.1000   -0.0006
   700        0.4618             nan     0.1000   -0.0003
   720        0.4555             nan     0.1000   -0.0010
   740        0.4511             nan     0.1000   -0.0008
   760        0.4478             nan     0.1000   -0.0007
   780        0.4440             nan     0.1000   -0.0010
   800        0.4401             nan     0.1000   -0.0003
   820        0.4360             nan     0.1000   -0.0010
   840        0.4308             nan     0.1000   -0.0008
   860        0.4274             nan     0.1000   -0.0004
   880        0.4225             nan     0.1000   -0.0006
   900        0.4209             nan     0.1000   -0.0010
   920        0.4175             nan     0.1000   -0.0012
   940        0.4151             nan     0.1000   -0.0009
   960        0.4123             nan     0.1000   -0.0013
   980        0.4105             nan     0.1000   -0.0003
  1000        0.4059             nan     0.1000   -0.0007
  1020        0.4028             nan     0.1000   -0.0010
  1040        0.3997             nan     0.1000   -0.0002
  1060        0.3974             nan     0.1000   -0.0005
  1080        0.3951             nan     0.1000   -0.0006
  1100        0.3931             nan     0.1000   -0.0014

- Fold10.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2473             nan     0.1000    0.0355
     2        1.1886             nan     0.1000    0.0298
     3        1.1347             nan     0.1000    0.0263
     4        1.0888             nan     0.1000    0.0219
     5        1.0539             nan     0.1000    0.0174
     6        1.0218             nan     0.1000    0.0151
     7        0.9910             nan     0.1000    0.0149
     8        0.9683             nan     0.1000    0.0103
     9        0.9519             nan     0.1000    0.0068
    10        0.9331             nan     0.1000    0.0065
    20        0.8269             nan     0.1000    0.0001
    40        0.7405             nan     0.1000   -0.0005
    60        0.7023             nan     0.1000   -0.0012
    80        0.6708             nan     0.1000   -0.0015
   100        0.6460             nan     0.1000   -0.0028
   120        0.6195             nan     0.1000   -0.0011
   140        0.5964             nan     0.1000   -0.0008
   160        0.5763             nan     0.1000   -0.0013
   180        0.5626             nan     0.1000   -0.0014
   200        0.5486             nan     0.1000   -0.0011
   220        0.5355             nan     0.1000   -0.0004
   240        0.5208             nan     0.1000   -0.0021
   260        0.5067             nan     0.1000   -0.0012
   280        0.4925             nan     0.1000   -0.0011
   300        0.4804             nan     0.1000   -0.0016
   320        0.4686             nan     0.1000   -0.0009
   340        0.4572             nan     0.1000   -0.0003
   360        0.4487             nan     0.1000   -0.0010
   380        0.4407             nan     0.1000   -0.0004
   400        0.4268             nan     0.1000   -0.0013
   420        0.4190             nan     0.1000   -0.0004
   440        0.4101             nan     0.1000   -0.0008
   460        0.4024             nan     0.1000   -0.0018
   480        0.3930             nan     0.1000   -0.0002
   500        0.3834             nan     0.1000   -0.0010
   520        0.3787             nan     0.1000   -0.0006
   540        0.3728             nan     0.1000   -0.0013
   560        0.3650             nan     0.1000   -0.0007
   580        0.3589             nan     0.1000   -0.0004
   600        0.3536             nan     0.1000   -0.0010
   620        0.3481             nan     0.1000   -0.0013
   640        0.3438             nan     0.1000   -0.0008
   660        0.3391             nan     0.1000   -0.0007
   680        0.3350             nan     0.1000   -0.0007
   700        0.3301             nan     0.1000   -0.0004
   720        0.3239             nan     0.1000   -0.0019
   740        0.3197             nan     0.1000   -0.0009
   760        0.3155             nan     0.1000   -0.0012
   780        0.3105             nan     0.1000   -0.0006
   800        0.3057             nan     0.1000   -0.0002
   820        0.3018             nan     0.1000   -0.0013
   840        0.2988             nan     0.1000   -0.0007
   860        0.2938             nan     0.1000   -0.0009
   880        0.2899             nan     0.1000   -0.0007
   900        0.2862             nan     0.1000   -0.0012
   920        0.2822             nan     0.1000   -0.0014
   940        0.2789             nan     0.1000   -0.0011
   960        0.2758             nan     0.1000   -0.0009
   980        0.2723             nan     0.1000   -0.0009
  1000        0.2692             nan     0.1000   -0.0004
  1020        0.2662             nan     0.1000   -0.0010
  1040        0.2632             nan     0.1000   -0.0010
  1060        0.2597             nan     0.1000   -0.0008
  1080        0.2562             nan     0.1000   -0.0004
  1100        0.2540             nan     0.1000   -0.0010

- Fold10.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3260             nan     0.0100    0.0030
     2        1.3195             nan     0.0100    0.0029
     3        1.3135             nan     0.0100    0.0029
     4        1.3076             nan     0.0100    0.0027
     5        1.3020             nan     0.0100    0.0027
     6        1.2967             nan     0.0100    0.0026
     7        1.2917             nan     0.0100    0.0026
     8        1.2867             nan     0.0100    0.0026
     9        1.2817             nan     0.0100    0.0026
    10        1.2767             nan     0.0100    0.0025
    20        1.2316             nan     0.0100    0.0021
    40        1.1623             nan     0.0100    0.0015
    60        1.1120             nan     0.0100    0.0010
    80        1.0744             nan     0.0100    0.0008
   100        1.0445             nan     0.0100    0.0006
   120        1.0204             nan     0.0100    0.0005
   140        1.0010             nan     0.0100    0.0003
   160        0.9835             nan     0.0100    0.0003
   180        0.9685             nan     0.0100    0.0003
   200        0.9560             nan     0.0100    0.0002
   220        0.9446             nan     0.0100    0.0002
   240        0.9345             nan     0.0100    0.0001
   260        0.9254             nan     0.0100    0.0001
   280        0.9169             nan     0.0100    0.0001
   300        0.9092             nan     0.0100    0.0001
   320        0.9022             nan     0.0100    0.0000
   340        0.8964             nan     0.0100    0.0000
   360        0.8902             nan     0.0100    0.0000
   380        0.8847             nan     0.0100   -0.0001
   400        0.8794             nan     0.0100   -0.0000
   420        0.8744             nan     0.0100   -0.0000
   440        0.8702             nan     0.0100    0.0001
   460        0.8654             nan     0.0100    0.0000
   480        0.8614             nan     0.0100    0.0000
   500        0.8573             nan     0.0100    0.0001
   520        0.8534             nan     0.0100    0.0001
   540        0.8500             nan     0.0100    0.0000
   560        0.8464             nan     0.0100    0.0000
   580        0.8429             nan     0.0100   -0.0000
   600        0.8397             nan     0.0100    0.0000
   620        0.8366             nan     0.0100   -0.0000
   640        0.8335             nan     0.0100   -0.0000
   660        0.8306             nan     0.0100    0.0000
   680        0.8280             nan     0.0100   -0.0000
   700        0.8252             nan     0.0100   -0.0000
   720        0.8227             nan     0.0100    0.0000
   740        0.8202             nan     0.0100    0.0000
   760        0.8177             nan     0.0100   -0.0001
   780        0.8153             nan     0.0100   -0.0000
   800        0.8129             nan     0.0100   -0.0001
   820        0.8107             nan     0.0100   -0.0000
   840        0.8085             nan     0.0100   -0.0000
   860        0.8064             nan     0.0100    0.0000
   880        0.8045             nan     0.0100   -0.0000
   900        0.8025             nan     0.0100   -0.0000
   920        0.8009             nan     0.0100   -0.0000
   940        0.7994             nan     0.0100   -0.0001
   960        0.7975             nan     0.0100    0.0000
   980        0.7958             nan     0.0100   -0.0001
  1000        0.7941             nan     0.0100   -0.0000
  1020        0.7927             nan     0.0100   -0.0000
  1040        0.7911             nan     0.0100   -0.0000
  1060        0.7895             nan     0.0100   -0.0001
  1080        0.7880             nan     0.0100   -0.0001
  1100        0.7866             nan     0.0100    0.0000

- Fold01.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0038
     2        1.3172             nan     0.0100    0.0036
     3        1.3100             nan     0.0100    0.0035
     4        1.3040             nan     0.0100    0.0029
     5        1.2970             nan     0.0100    0.0034
     6        1.2903             nan     0.0100    0.0033
     7        1.2837             nan     0.0100    0.0033
     8        1.2773             nan     0.0100    0.0033
     9        1.2708             nan     0.0100    0.0030
    10        1.2643             nan     0.0100    0.0031
    20        1.2077             nan     0.0100    0.0026
    40        1.1197             nan     0.0100    0.0018
    60        1.0541             nan     0.0100    0.0013
    80        1.0056             nan     0.0100    0.0008
   100        0.9700             nan     0.0100    0.0007
   120        0.9416             nan     0.0100    0.0003
   140        0.9186             nan     0.0100    0.0005
   160        0.9001             nan     0.0100    0.0003
   180        0.8853             nan     0.0100    0.0001
   200        0.8719             nan     0.0100    0.0001
   220        0.8597             nan     0.0100    0.0001
   240        0.8496             nan     0.0100    0.0001
   260        0.8407             nan     0.0100    0.0001
   280        0.8328             nan     0.0100    0.0000
   300        0.8256             nan     0.0100    0.0001
   320        0.8187             nan     0.0100    0.0002
   340        0.8123             nan     0.0100    0.0000
   360        0.8055             nan     0.0100    0.0000
   380        0.7996             nan     0.0100    0.0001
   400        0.7939             nan     0.0100    0.0001
   420        0.7887             nan     0.0100    0.0000
   440        0.7841             nan     0.0100   -0.0000
   460        0.7797             nan     0.0100    0.0000
   480        0.7752             nan     0.0100   -0.0000
   500        0.7715             nan     0.0100   -0.0001
   520        0.7673             nan     0.0100   -0.0001
   540        0.7638             nan     0.0100   -0.0001
   560        0.7601             nan     0.0100   -0.0000
   580        0.7565             nan     0.0100   -0.0001
   600        0.7532             nan     0.0100   -0.0001
   620        0.7493             nan     0.0100   -0.0000
   640        0.7465             nan     0.0100   -0.0001
   660        0.7434             nan     0.0100   -0.0001
   680        0.7403             nan     0.0100   -0.0000
   700        0.7373             nan     0.0100   -0.0000
   720        0.7346             nan     0.0100   -0.0000
   740        0.7322             nan     0.0100   -0.0001
   760        0.7298             nan     0.0100   -0.0000
   780        0.7274             nan     0.0100   -0.0001
   800        0.7246             nan     0.0100   -0.0002
   820        0.7225             nan     0.0100   -0.0001
   840        0.7195             nan     0.0100   -0.0001
   860        0.7170             nan     0.0100   -0.0001
   880        0.7148             nan     0.0100   -0.0001
   900        0.7127             nan     0.0100   -0.0001
   920        0.7107             nan     0.0100    0.0000
   940        0.7089             nan     0.0100   -0.0001
   960        0.7065             nan     0.0100   -0.0000
   980        0.7046             nan     0.0100   -0.0001
  1000        0.7026             nan     0.0100   -0.0001
  1020        0.7005             nan     0.0100   -0.0001
  1040        0.6985             nan     0.0100   -0.0002
  1060        0.6963             nan     0.0100   -0.0001
  1080        0.6942             nan     0.0100   -0.0001
  1100        0.6923             nan     0.0100   -0.0001

- Fold01.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0040
     2        1.3166             nan     0.0100    0.0039
     3        1.3084             nan     0.0100    0.0039
     4        1.3009             nan     0.0100    0.0038
     5        1.2936             nan     0.0100    0.0036
     6        1.2860             nan     0.0100    0.0036
     7        1.2786             nan     0.0100    0.0034
     8        1.2715             nan     0.0100    0.0032
     9        1.2642             nan     0.0100    0.0035
    10        1.2575             nan     0.0100    0.0033
    20        1.1953             nan     0.0100    0.0027
    40        1.0993             nan     0.0100    0.0020
    60        1.0290             nan     0.0100    0.0014
    80        0.9763             nan     0.0100    0.0009
   100        0.9373             nan     0.0100    0.0007
   120        0.9070             nan     0.0100    0.0005
   140        0.8839             nan     0.0100    0.0003
   160        0.8639             nan     0.0100    0.0004
   180        0.8477             nan     0.0100    0.0003
   200        0.8340             nan     0.0100    0.0001
   220        0.8216             nan     0.0100    0.0001
   240        0.8105             nan     0.0100    0.0001
   260        0.7998             nan     0.0100    0.0001
   280        0.7913             nan     0.0100    0.0000
   300        0.7833             nan     0.0100   -0.0001
   320        0.7754             nan     0.0100   -0.0001
   340        0.7678             nan     0.0100   -0.0001
   360        0.7610             nan     0.0100   -0.0001
   380        0.7557             nan     0.0100   -0.0000
   400        0.7505             nan     0.0100   -0.0001
   420        0.7456             nan     0.0100   -0.0000
   440        0.7404             nan     0.0100   -0.0001
   460        0.7354             nan     0.0100    0.0000
   480        0.7307             nan     0.0100   -0.0001
   500        0.7260             nan     0.0100   -0.0001
   520        0.7216             nan     0.0100   -0.0001
   540        0.7176             nan     0.0100   -0.0000
   560        0.7134             nan     0.0100    0.0000
   580        0.7099             nan     0.0100    0.0000
   600        0.7064             nan     0.0100   -0.0001
   620        0.7028             nan     0.0100   -0.0001
   640        0.6991             nan     0.0100   -0.0000
   660        0.6958             nan     0.0100   -0.0000
   680        0.6918             nan     0.0100   -0.0001
   700        0.6885             nan     0.0100   -0.0001
   720        0.6850             nan     0.0100   -0.0000
   740        0.6819             nan     0.0100   -0.0002
   760        0.6786             nan     0.0100   -0.0000
   780        0.6753             nan     0.0100   -0.0001
   800        0.6721             nan     0.0100   -0.0000
   820        0.6690             nan     0.0100   -0.0001
   840        0.6657             nan     0.0100   -0.0001
   860        0.6626             nan     0.0100   -0.0001
   880        0.6600             nan     0.0100   -0.0001
   900        0.6572             nan     0.0100   -0.0000
   920        0.6540             nan     0.0100   -0.0001
   940        0.6517             nan     0.0100   -0.0001
   960        0.6487             nan     0.0100   -0.0001
   980        0.6457             nan     0.0100   -0.0001
  1000        0.6430             nan     0.0100   -0.0001
  1020        0.6404             nan     0.0100   -0.0002
  1040        0.6378             nan     0.0100   -0.0001
  1060        0.6353             nan     0.0100   -0.0002
  1080        0.6323             nan     0.0100   -0.0001
  1100        0.6300             nan     0.0100   -0.0001

- Fold01.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2785             nan     0.1000    0.0276
     2        1.2274             nan     0.1000    0.0239
     3        1.1877             nan     0.1000    0.0185
     4        1.1595             nan     0.1000    0.0161
     5        1.1335             nan     0.1000    0.0136
     6        1.1099             nan     0.1000    0.0110
     7        1.0916             nan     0.1000    0.0085
     8        1.0737             nan     0.1000    0.0065
     9        1.0553             nan     0.1000    0.0076
    10        1.0411             nan     0.1000    0.0062
    20        0.9542             nan     0.1000    0.0017
    40        0.8788             nan     0.1000    0.0005
    60        0.8385             nan     0.1000   -0.0001
    80        0.8127             nan     0.1000   -0.0010
   100        0.7938             nan     0.1000   -0.0005
   120        0.7812             nan     0.1000   -0.0011
   140        0.7706             nan     0.1000   -0.0008
   160        0.7632             nan     0.1000   -0.0018
   180        0.7548             nan     0.1000   -0.0011
   200        0.7484             nan     0.1000   -0.0004
   220        0.7429             nan     0.1000   -0.0013
   240        0.7363             nan     0.1000   -0.0008
   260        0.7320             nan     0.1000   -0.0004
   280        0.7267             nan     0.1000   -0.0009
   300        0.7230             nan     0.1000   -0.0014
   320        0.7187             nan     0.1000   -0.0007
   340        0.7147             nan     0.1000   -0.0010
   360        0.7122             nan     0.1000   -0.0002
   380        0.7094             nan     0.1000   -0.0008
   400        0.7059             nan     0.1000   -0.0006
   420        0.7019             nan     0.1000   -0.0007
   440        0.6990             nan     0.1000   -0.0006
   460        0.6960             nan     0.1000   -0.0005
   480        0.6914             nan     0.1000   -0.0004
   500        0.6895             nan     0.1000   -0.0010
   520        0.6867             nan     0.1000   -0.0010
   540        0.6854             nan     0.1000   -0.0009
   560        0.6818             nan     0.1000   -0.0013
   580        0.6777             nan     0.1000   -0.0007
   600        0.6753             nan     0.1000   -0.0006
   620        0.6730             nan     0.1000   -0.0006
   640        0.6717             nan     0.1000   -0.0007
   660        0.6686             nan     0.1000   -0.0007
   680        0.6663             nan     0.1000   -0.0006
   700        0.6649             nan     0.1000   -0.0006
   720        0.6627             nan     0.1000   -0.0008
   740        0.6620             nan     0.1000   -0.0008
   760        0.6606             nan     0.1000   -0.0005
   780        0.6585             nan     0.1000   -0.0008
   800        0.6570             nan     0.1000   -0.0004
   820        0.6557             nan     0.1000   -0.0006
   840        0.6539             nan     0.1000   -0.0008
   860        0.6524             nan     0.1000   -0.0006
   880        0.6509             nan     0.1000   -0.0014
   900        0.6492             nan     0.1000   -0.0005
   920        0.6482             nan     0.1000   -0.0007
   940        0.6466             nan     0.1000   -0.0013
   960        0.6461             nan     0.1000   -0.0005
   980        0.6440             nan     0.1000   -0.0003
  1000        0.6412             nan     0.1000   -0.0007
  1020        0.6397             nan     0.1000   -0.0008
  1040        0.6379             nan     0.1000   -0.0003
  1060        0.6365             nan     0.1000   -0.0008
  1080        0.6363             nan     0.1000   -0.0006
  1100        0.6341             nan     0.1000   -0.0012

- Fold01.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2587             nan     0.1000    0.0351
     2        1.1984             nan     0.1000    0.0292
     3        1.1488             nan     0.1000    0.0209
     4        1.1040             nan     0.1000    0.0201
     5        1.0740             nan     0.1000    0.0160
     6        1.0465             nan     0.1000    0.0089
     7        1.0200             nan     0.1000    0.0113
     8        0.9957             nan     0.1000    0.0107
     9        0.9771             nan     0.1000    0.0080
    10        0.9626             nan     0.1000    0.0074
    20        0.8749             nan     0.1000    0.0009
    40        0.7982             nan     0.1000    0.0000
    60        0.7625             nan     0.1000   -0.0007
    80        0.7337             nan     0.1000   -0.0004
   100        0.7115             nan     0.1000   -0.0007
   120        0.6930             nan     0.1000    0.0002
   140        0.6770             nan     0.1000   -0.0003
   160        0.6600             nan     0.1000   -0.0006
   180        0.6476             nan     0.1000   -0.0008
   200        0.6358             nan     0.1000   -0.0010
   220        0.6243             nan     0.1000   -0.0006
   240        0.6140             nan     0.1000   -0.0017
   260        0.6015             nan     0.1000   -0.0010
   280        0.5912             nan     0.1000   -0.0008
   300        0.5843             nan     0.1000   -0.0011
   320        0.5728             nan     0.1000   -0.0010
   340        0.5649             nan     0.1000   -0.0008
   360        0.5573             nan     0.1000   -0.0006
   380        0.5495             nan     0.1000   -0.0006
   400        0.5418             nan     0.1000   -0.0009
   420        0.5343             nan     0.1000   -0.0013
   440        0.5246             nan     0.1000   -0.0014
   460        0.5169             nan     0.1000   -0.0012
   480        0.5104             nan     0.1000   -0.0015
   500        0.5058             nan     0.1000   -0.0004
   520        0.5021             nan     0.1000   -0.0016
   540        0.4955             nan     0.1000   -0.0008
   560        0.4906             nan     0.1000   -0.0009
   580        0.4856             nan     0.1000   -0.0014
   600        0.4778             nan     0.1000   -0.0008
   620        0.4710             nan     0.1000   -0.0016
   640        0.4671             nan     0.1000   -0.0009
   660        0.4631             nan     0.1000   -0.0012
   680        0.4580             nan     0.1000   -0.0012
   700        0.4543             nan     0.1000   -0.0004
   720        0.4509             nan     0.1000   -0.0008
   740        0.4478             nan     0.1000   -0.0007
   760        0.4452             nan     0.1000   -0.0014
   780        0.4403             nan     0.1000   -0.0007
   800        0.4356             nan     0.1000   -0.0010
   820        0.4320             nan     0.1000   -0.0008
   840        0.4284             nan     0.1000   -0.0011
   860        0.4255             nan     0.1000   -0.0008
   880        0.4205             nan     0.1000   -0.0004
   900        0.4175             nan     0.1000   -0.0005
   920        0.4125             nan     0.1000   -0.0012
   940        0.4078             nan     0.1000   -0.0005
   960        0.4042             nan     0.1000   -0.0015
   980        0.4013             nan     0.1000   -0.0004
  1000        0.3984             nan     0.1000   -0.0004
  1020        0.3945             nan     0.1000   -0.0005
  1040        0.3908             nan     0.1000   -0.0008
  1060        0.3892             nan     0.1000   -0.0009
  1080        0.3865             nan     0.1000   -0.0010
  1100        0.3836             nan     0.1000   -0.0014

- Fold01.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2533             nan     0.1000    0.0410
     2        1.1869             nan     0.1000    0.0295
     3        1.1342             nan     0.1000    0.0252
     4        1.0888             nan     0.1000    0.0223
     5        1.0517             nan     0.1000    0.0172
     6        1.0204             nan     0.1000    0.0155
     7        0.9906             nan     0.1000    0.0123
     8        0.9647             nan     0.1000    0.0110
     9        0.9445             nan     0.1000    0.0083
    10        0.9260             nan     0.1000    0.0074
    20        0.8262             nan     0.1000    0.0024
    40        0.7448             nan     0.1000    0.0008
    60        0.7079             nan     0.1000   -0.0004
    80        0.6712             nan     0.1000   -0.0008
   100        0.6445             nan     0.1000   -0.0007
   120        0.6183             nan     0.1000   -0.0017
   140        0.5923             nan     0.1000   -0.0010
   160        0.5744             nan     0.1000   -0.0019
   180        0.5587             nan     0.1000   -0.0017
   200        0.5411             nan     0.1000   -0.0013
   220        0.5281             nan     0.1000   -0.0012
   240        0.5137             nan     0.1000   -0.0016
   260        0.5004             nan     0.1000   -0.0012
   280        0.4852             nan     0.1000   -0.0019
   300        0.4747             nan     0.1000   -0.0009
   320        0.4664             nan     0.1000   -0.0016
   340        0.4552             nan     0.1000   -0.0014
   360        0.4439             nan     0.1000   -0.0012
   380        0.4348             nan     0.1000   -0.0016
   400        0.4239             nan     0.1000   -0.0011
   420        0.4167             nan     0.1000   -0.0015
   440        0.4088             nan     0.1000   -0.0002
   460        0.3992             nan     0.1000   -0.0017
   480        0.3898             nan     0.1000   -0.0003
   500        0.3837             nan     0.1000   -0.0009
   520        0.3770             nan     0.1000   -0.0009
   540        0.3701             nan     0.1000   -0.0012
   560        0.3642             nan     0.1000   -0.0014
   580        0.3587             nan     0.1000   -0.0004
   600        0.3520             nan     0.1000   -0.0004
   620        0.3462             nan     0.1000   -0.0009
   640        0.3411             nan     0.1000   -0.0009
   660        0.3350             nan     0.1000   -0.0004
   680        0.3277             nan     0.1000   -0.0007
   700        0.3246             nan     0.1000   -0.0011
   720        0.3193             nan     0.1000   -0.0008
   740        0.3153             nan     0.1000   -0.0010
   760        0.3114             nan     0.1000   -0.0005
   780        0.3067             nan     0.1000   -0.0009
   800        0.3016             nan     0.1000   -0.0005
   820        0.2979             nan     0.1000   -0.0010
   840        0.2947             nan     0.1000   -0.0011
   860        0.2891             nan     0.1000   -0.0005
   880        0.2838             nan     0.1000   -0.0008
   900        0.2808             nan     0.1000   -0.0008
   920        0.2770             nan     0.1000   -0.0006
   940        0.2733             nan     0.1000   -0.0005
   960        0.2696             nan     0.1000   -0.0013
   980        0.2667             nan     0.1000   -0.0003
  1000        0.2636             nan     0.1000   -0.0004
  1020        0.2601             nan     0.1000   -0.0004
  1040        0.2575             nan     0.1000   -0.0007
  1060        0.2543             nan     0.1000   -0.0005
  1080        0.2516             nan     0.1000   -0.0006
  1100        0.2485             nan     0.1000   -0.0003

- Fold01.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3248             nan     0.0100    0.0032
     2        1.3190             nan     0.0100    0.0029
     3        1.3134             nan     0.0100    0.0029
     4        1.3076             nan     0.0100    0.0028
     5        1.3019             nan     0.0100    0.0029
     6        1.2958             nan     0.0100    0.0028
     7        1.2897             nan     0.0100    0.0028
     8        1.2840             nan     0.0100    0.0028
     9        1.2789             nan     0.0100    0.0025
    10        1.2735             nan     0.0100    0.0026
    20        1.2264             nan     0.0100    0.0021
    40        1.1545             nan     0.0100    0.0015
    60        1.1046             nan     0.0100    0.0011
    80        1.0667             nan     0.0100    0.0008
   100        1.0370             nan     0.0100    0.0006
   120        1.0129             nan     0.0100    0.0005
   140        0.9922             nan     0.0100    0.0004
   160        0.9751             nan     0.0100    0.0003
   180        0.9609             nan     0.0100    0.0002
   200        0.9478             nan     0.0100    0.0002
   220        0.9359             nan     0.0100    0.0001
   240        0.9256             nan     0.0100    0.0001
   260        0.9168             nan     0.0100    0.0001
   280        0.9085             nan     0.0100   -0.0001
   300        0.9011             nan     0.0100    0.0001
   320        0.8940             nan     0.0100    0.0001
   340        0.8876             nan     0.0100    0.0001
   360        0.8816             nan     0.0100    0.0001
   380        0.8759             nan     0.0100    0.0000
   400        0.8708             nan     0.0100    0.0001
   420        0.8660             nan     0.0100    0.0001
   440        0.8614             nan     0.0100    0.0000
   460        0.8573             nan     0.0100    0.0001
   480        0.8534             nan     0.0100    0.0001
   500        0.8497             nan     0.0100    0.0000
   520        0.8463             nan     0.0100    0.0000
   540        0.8427             nan     0.0100   -0.0000
   560        0.8392             nan     0.0100   -0.0000
   580        0.8360             nan     0.0100   -0.0000
   600        0.8328             nan     0.0100   -0.0000
   620        0.8300             nan     0.0100    0.0000
   640        0.8271             nan     0.0100    0.0000
   660        0.8245             nan     0.0100   -0.0000
   680        0.8220             nan     0.0100   -0.0000
   700        0.8196             nan     0.0100   -0.0000
   720        0.8174             nan     0.0100   -0.0000
   740        0.8152             nan     0.0100   -0.0000
   760        0.8132             nan     0.0100   -0.0000
   780        0.8109             nan     0.0100   -0.0000
   800        0.8089             nan     0.0100   -0.0000
   820        0.8070             nan     0.0100   -0.0000
   840        0.8052             nan     0.0100   -0.0000
   860        0.8032             nan     0.0100   -0.0000
   880        0.8014             nan     0.0100   -0.0000
   900        0.7995             nan     0.0100   -0.0000
   920        0.7979             nan     0.0100   -0.0001
   940        0.7964             nan     0.0100   -0.0001
   960        0.7949             nan     0.0100   -0.0002
   980        0.7933             nan     0.0100   -0.0000
  1000        0.7918             nan     0.0100   -0.0000
  1020        0.7905             nan     0.0100   -0.0000
  1040        0.7890             nan     0.0100   -0.0000
  1060        0.7877             nan     0.0100   -0.0001
  1080        0.7863             nan     0.0100   -0.0000
  1100        0.7851             nan     0.0100   -0.0000

- Fold02.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3232             nan     0.0100    0.0036
     2        1.3161             nan     0.0100    0.0035
     3        1.3088             nan     0.0100    0.0034
     4        1.3013             nan     0.0100    0.0036
     5        1.2943             nan     0.0100    0.0034
     6        1.2872             nan     0.0100    0.0033
     7        1.2805             nan     0.0100    0.0031
     8        1.2739             nan     0.0100    0.0032
     9        1.2677             nan     0.0100    0.0029
    10        1.2615             nan     0.0100    0.0031
    20        1.2055             nan     0.0100    0.0023
    40        1.1172             nan     0.0100    0.0019
    60        1.0519             nan     0.0100    0.0014
    80        1.0032             nan     0.0100    0.0010
   100        0.9662             nan     0.0100    0.0006
   120        0.9369             nan     0.0100    0.0005
   140        0.9143             nan     0.0100    0.0003
   160        0.8959             nan     0.0100    0.0004
   180        0.8806             nan     0.0100    0.0001
   200        0.8676             nan     0.0100    0.0002
   220        0.8558             nan     0.0100    0.0001
   240        0.8454             nan     0.0100    0.0003
   260        0.8373             nan     0.0100   -0.0001
   280        0.8291             nan     0.0100    0.0001
   300        0.8220             nan     0.0100   -0.0001
   320        0.8155             nan     0.0100   -0.0001
   340        0.8092             nan     0.0100   -0.0000
   360        0.8035             nan     0.0100    0.0000
   380        0.7976             nan     0.0100    0.0001
   400        0.7929             nan     0.0100   -0.0001
   420        0.7875             nan     0.0100   -0.0000
   440        0.7822             nan     0.0100   -0.0000
   460        0.7783             nan     0.0100   -0.0000
   480        0.7742             nan     0.0100   -0.0001
   500        0.7699             nan     0.0100   -0.0001
   520        0.7663             nan     0.0100   -0.0000
   540        0.7631             nan     0.0100   -0.0000
   560        0.7597             nan     0.0100   -0.0000
   580        0.7566             nan     0.0100   -0.0001
   600        0.7538             nan     0.0100   -0.0001
   620        0.7510             nan     0.0100   -0.0001
   640        0.7481             nan     0.0100   -0.0001
   660        0.7454             nan     0.0100   -0.0001
   680        0.7426             nan     0.0100   -0.0000
   700        0.7399             nan     0.0100   -0.0000
   720        0.7374             nan     0.0100   -0.0000
   740        0.7349             nan     0.0100   -0.0001
   760        0.7326             nan     0.0100   -0.0002
   780        0.7299             nan     0.0100   -0.0001
   800        0.7271             nan     0.0100   -0.0000
   820        0.7249             nan     0.0100   -0.0001
   840        0.7230             nan     0.0100   -0.0001
   860        0.7212             nan     0.0100   -0.0001
   880        0.7190             nan     0.0100   -0.0001
   900        0.7166             nan     0.0100   -0.0001
   920        0.7145             nan     0.0100   -0.0001
   940        0.7126             nan     0.0100   -0.0001
   960        0.7107             nan     0.0100    0.0000
   980        0.7087             nan     0.0100   -0.0001
  1000        0.7065             nan     0.0100   -0.0001
  1020        0.7044             nan     0.0100   -0.0001
  1040        0.7025             nan     0.0100   -0.0001
  1060        0.7009             nan     0.0100   -0.0001
  1080        0.6990             nan     0.0100    0.0000
  1100        0.6974             nan     0.0100   -0.0001

- Fold02.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3237             nan     0.0100    0.0035
     2        1.3157             nan     0.0100    0.0040
     3        1.3082             nan     0.0100    0.0036
     4        1.3006             nan     0.0100    0.0034
     5        1.2932             nan     0.0100    0.0035
     6        1.2863             nan     0.0100    0.0036
     7        1.2787             nan     0.0100    0.0036
     8        1.2713             nan     0.0100    0.0037
     9        1.2646             nan     0.0100    0.0034
    10        1.2574             nan     0.0100    0.0033
    20        1.1954             nan     0.0100    0.0027
    40        1.0982             nan     0.0100    0.0021
    60        1.0277             nan     0.0100    0.0015
    80        0.9752             nan     0.0100    0.0011
   100        0.9347             nan     0.0100    0.0007
   120        0.9050             nan     0.0100    0.0005
   140        0.8804             nan     0.0100    0.0003
   160        0.8618             nan     0.0100    0.0005
   180        0.8453             nan     0.0100    0.0001
   200        0.8304             nan     0.0100    0.0002
   220        0.8176             nan     0.0100    0.0001
   240        0.8071             nan     0.0100   -0.0001
   260        0.7972             nan     0.0100    0.0001
   280        0.7886             nan     0.0100    0.0002
   300        0.7811             nan     0.0100    0.0000
   320        0.7739             nan     0.0100    0.0001
   340        0.7666             nan     0.0100   -0.0000
   360        0.7599             nan     0.0100    0.0001
   380        0.7539             nan     0.0100    0.0001
   400        0.7489             nan     0.0100   -0.0000
   420        0.7444             nan     0.0100   -0.0000
   440        0.7400             nan     0.0100   -0.0001
   460        0.7354             nan     0.0100   -0.0002
   480        0.7313             nan     0.0100   -0.0002
   500        0.7269             nan     0.0100   -0.0000
   520        0.7230             nan     0.0100   -0.0001
   540        0.7193             nan     0.0100   -0.0001
   560        0.7156             nan     0.0100   -0.0001
   580        0.7117             nan     0.0100   -0.0001
   600        0.7078             nan     0.0100   -0.0001
   620        0.7047             nan     0.0100   -0.0001
   640        0.7010             nan     0.0100   -0.0001
   660        0.6972             nan     0.0100   -0.0001
   680        0.6936             nan     0.0100   -0.0000
   700        0.6902             nan     0.0100   -0.0002
   720        0.6870             nan     0.0100   -0.0001
   740        0.6838             nan     0.0100   -0.0001
   760        0.6807             nan     0.0100   -0.0001
   780        0.6778             nan     0.0100    0.0000
   800        0.6748             nan     0.0100   -0.0001
   820        0.6721             nan     0.0100   -0.0001
   840        0.6696             nan     0.0100   -0.0001
   860        0.6668             nan     0.0100   -0.0000
   880        0.6639             nan     0.0100   -0.0001
   900        0.6612             nan     0.0100   -0.0001
   920        0.6588             nan     0.0100   -0.0000
   940        0.6565             nan     0.0100   -0.0000
   960        0.6535             nan     0.0100   -0.0001
   980        0.6507             nan     0.0100   -0.0000
  1000        0.6484             nan     0.0100   -0.0000
  1020        0.6458             nan     0.0100   -0.0001
  1040        0.6433             nan     0.0100   -0.0001
  1060        0.6413             nan     0.0100   -0.0001
  1080        0.6387             nan     0.0100   -0.0001
  1100        0.6359             nan     0.0100   -0.0001

- Fold02.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2741             nan     0.1000    0.0291
     2        1.2222             nan     0.1000    0.0249
     3        1.1852             nan     0.1000    0.0191
     4        1.1559             nan     0.1000    0.0160
     5        1.1268             nan     0.1000    0.0145
     6        1.1038             nan     0.1000    0.0124
     7        1.0860             nan     0.1000    0.0067
     8        1.0630             nan     0.1000    0.0091
     9        1.0484             nan     0.1000    0.0074
    10        1.0332             nan     0.1000    0.0050
    20        0.9444             nan     0.1000    0.0005
    40        0.8694             nan     0.1000    0.0004
    60        0.8321             nan     0.1000   -0.0002
    80        0.8101             nan     0.1000   -0.0005
   100        0.7953             nan     0.1000   -0.0007
   120        0.7820             nan     0.1000    0.0002
   140        0.7723             nan     0.1000   -0.0007
   160        0.7614             nan     0.1000   -0.0014
   180        0.7537             nan     0.1000   -0.0014
   200        0.7468             nan     0.1000   -0.0003
   220        0.7422             nan     0.1000   -0.0010
   240        0.7360             nan     0.1000   -0.0007
   260        0.7317             nan     0.1000   -0.0005
   280        0.7272             nan     0.1000   -0.0006
   300        0.7242             nan     0.1000   -0.0005
   320        0.7203             nan     0.1000   -0.0010
   340        0.7182             nan     0.1000   -0.0004
   360        0.7147             nan     0.1000   -0.0008
   380        0.7114             nan     0.1000   -0.0005
   400        0.7077             nan     0.1000   -0.0010
   420        0.7051             nan     0.1000   -0.0018
   440        0.7017             nan     0.1000   -0.0015
   460        0.6984             nan     0.1000   -0.0007
   480        0.6964             nan     0.1000   -0.0006
   500        0.6938             nan     0.1000   -0.0010
   520        0.6909             nan     0.1000   -0.0024
   540        0.6890             nan     0.1000   -0.0008
   560        0.6868             nan     0.1000   -0.0009
   580        0.6835             nan     0.1000   -0.0005
   600        0.6816             nan     0.1000   -0.0005
   620        0.6794             nan     0.1000   -0.0004
   640        0.6771             nan     0.1000   -0.0009
   660        0.6759             nan     0.1000   -0.0026
   680        0.6751             nan     0.1000   -0.0002
   700        0.6734             nan     0.1000   -0.0011
   720        0.6706             nan     0.1000   -0.0011
   740        0.6695             nan     0.1000   -0.0013
   760        0.6674             nan     0.1000   -0.0006
   780        0.6658             nan     0.1000   -0.0005
   800        0.6640             nan     0.1000   -0.0006
   820        0.6628             nan     0.1000   -0.0005
   840        0.6615             nan     0.1000   -0.0006
   860        0.6594             nan     0.1000   -0.0004
   880        0.6578             nan     0.1000   -0.0008
   900        0.6568             nan     0.1000   -0.0004
   920        0.6560             nan     0.1000   -0.0006
   940        0.6551             nan     0.1000   -0.0005
   960        0.6543             nan     0.1000   -0.0007
   980        0.6524             nan     0.1000   -0.0004
  1000        0.6503             nan     0.1000   -0.0010
  1020        0.6485             nan     0.1000   -0.0006
  1040        0.6466             nan     0.1000   -0.0008
  1060        0.6459             nan     0.1000   -0.0012
  1080        0.6448             nan     0.1000   -0.0009
  1100        0.6434             nan     0.1000   -0.0011

- Fold02.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2573             nan     0.1000    0.0352
     2        1.1959             nan     0.1000    0.0268
     3        1.1452             nan     0.1000    0.0212
     4        1.1047             nan     0.1000    0.0194
     5        1.0672             nan     0.1000    0.0162
     6        1.0419             nan     0.1000    0.0128
     7        1.0149             nan     0.1000    0.0113
     8        0.9962             nan     0.1000    0.0066
     9        0.9758             nan     0.1000    0.0090
    10        0.9628             nan     0.1000    0.0053
    20        0.8647             nan     0.1000    0.0019
    40        0.7868             nan     0.1000    0.0014
    60        0.7510             nan     0.1000    0.0002
    80        0.7246             nan     0.1000   -0.0008
   100        0.7024             nan     0.1000   -0.0005
   120        0.6853             nan     0.1000   -0.0002
   140        0.6692             nan     0.1000   -0.0012
   160        0.6521             nan     0.1000   -0.0007
   180        0.6400             nan     0.1000   -0.0005
   200        0.6296             nan     0.1000   -0.0007
   220        0.6201             nan     0.1000   -0.0001
   240        0.6104             nan     0.1000   -0.0014
   260        0.6009             nan     0.1000   -0.0012
   280        0.5925             nan     0.1000   -0.0012
   300        0.5831             nan     0.1000   -0.0010
   320        0.5766             nan     0.1000   -0.0007
   340        0.5692             nan     0.1000   -0.0002
   360        0.5618             nan     0.1000   -0.0006
   380        0.5534             nan     0.1000   -0.0017
   400        0.5450             nan     0.1000   -0.0016
   420        0.5381             nan     0.1000   -0.0009
   440        0.5287             nan     0.1000   -0.0003
   460        0.5210             nan     0.1000   -0.0012
   480        0.5143             nan     0.1000   -0.0001
   500        0.5074             nan     0.1000   -0.0010
   520        0.5013             nan     0.1000   -0.0012
   540        0.4949             nan     0.1000   -0.0012
   560        0.4910             nan     0.1000   -0.0010
   580        0.4854             nan     0.1000   -0.0007
   600        0.4781             nan     0.1000   -0.0004
   620        0.4729             nan     0.1000   -0.0009
   640        0.4683             nan     0.1000   -0.0013
   660        0.4640             nan     0.1000   -0.0010
   680        0.4592             nan     0.1000   -0.0009
   700        0.4533             nan     0.1000   -0.0011
   720        0.4484             nan     0.1000   -0.0013
   740        0.4416             nan     0.1000   -0.0010
   760        0.4386             nan     0.1000   -0.0009
   780        0.4344             nan     0.1000   -0.0009
   800        0.4313             nan     0.1000   -0.0004
   820        0.4278             nan     0.1000   -0.0011
   840        0.4233             nan     0.1000   -0.0003
   860        0.4196             nan     0.1000   -0.0007
   880        0.4136             nan     0.1000   -0.0008
   900        0.4093             nan     0.1000   -0.0008
   920        0.4060             nan     0.1000   -0.0013
   940        0.4029             nan     0.1000   -0.0005
   960        0.3991             nan     0.1000   -0.0006
   980        0.3959             nan     0.1000   -0.0006
  1000        0.3916             nan     0.1000   -0.0010
  1020        0.3888             nan     0.1000   -0.0011
  1040        0.3852             nan     0.1000   -0.0009
  1060        0.3825             nan     0.1000   -0.0007
  1080        0.3786             nan     0.1000   -0.0007
  1100        0.3766             nan     0.1000   -0.0005

- Fold02.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2540             nan     0.1000    0.0404
     2        1.1898             nan     0.1000    0.0310
     3        1.1307             nan     0.1000    0.0264
     4        1.0856             nan     0.1000    0.0225
     5        1.0468             nan     0.1000    0.0164
     6        1.0195             nan     0.1000    0.0114
     7        0.9902             nan     0.1000    0.0138
     8        0.9675             nan     0.1000    0.0115
     9        0.9454             nan     0.1000    0.0100
    10        0.9263             nan     0.1000    0.0093
    20        0.8282             nan     0.1000    0.0028
    40        0.7508             nan     0.1000   -0.0002
    60        0.7060             nan     0.1000   -0.0011
    80        0.6740             nan     0.1000   -0.0009
   100        0.6533             nan     0.1000   -0.0002
   120        0.6341             nan     0.1000   -0.0014
   140        0.6123             nan     0.1000   -0.0002
   160        0.5922             nan     0.1000   -0.0008
   180        0.5730             nan     0.1000   -0.0012
   200        0.5554             nan     0.1000   -0.0007
   220        0.5409             nan     0.1000   -0.0008
   240        0.5282             nan     0.1000   -0.0012
   260        0.5158             nan     0.1000   -0.0003
   280        0.5024             nan     0.1000   -0.0012
   300        0.4931             nan     0.1000   -0.0014
   320        0.4828             nan     0.1000   -0.0018
   340        0.4707             nan     0.1000   -0.0008
   360        0.4614             nan     0.1000   -0.0013
   380        0.4514             nan     0.1000   -0.0012
   400        0.4433             nan     0.1000   -0.0018
   420        0.4369             nan     0.1000   -0.0008
   440        0.4290             nan     0.1000   -0.0005
   460        0.4200             nan     0.1000   -0.0003
   480        0.4125             nan     0.1000   -0.0010
   500        0.4042             nan     0.1000   -0.0005
   520        0.3974             nan     0.1000   -0.0004
   540        0.3902             nan     0.1000   -0.0008
   560        0.3838             nan     0.1000   -0.0016
   580        0.3765             nan     0.1000   -0.0005
   600        0.3697             nan     0.1000   -0.0010
   620        0.3655             nan     0.1000   -0.0003
   640        0.3587             nan     0.1000   -0.0015
   660        0.3545             nan     0.1000   -0.0014
   680        0.3472             nan     0.1000   -0.0002
   700        0.3420             nan     0.1000   -0.0013
   720        0.3359             nan     0.1000   -0.0009
   740        0.3323             nan     0.1000   -0.0016
   760        0.3272             nan     0.1000   -0.0012
   780        0.3241             nan     0.1000   -0.0005
   800        0.3175             nan     0.1000   -0.0007
   820        0.3135             nan     0.1000   -0.0009
   840        0.3076             nan     0.1000   -0.0006
   860        0.3039             nan     0.1000   -0.0007
   880        0.2984             nan     0.1000   -0.0010
   900        0.2936             nan     0.1000   -0.0010
   920        0.2879             nan     0.1000   -0.0004
   940        0.2839             nan     0.1000   -0.0007
   960        0.2797             nan     0.1000   -0.0007
   980        0.2763             nan     0.1000   -0.0006
  1000        0.2723             nan     0.1000   -0.0008
  1020        0.2697             nan     0.1000   -0.0010
  1040        0.2660             nan     0.1000   -0.0007
  1060        0.2632             nan     0.1000   -0.0006
  1080        0.2610             nan     0.1000   -0.0008
  1100        0.2579             nan     0.1000   -0.0007

- Fold02.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3261             nan     0.0100    0.0028
     2        1.3206             nan     0.0100    0.0028
     3        1.3153             nan     0.0100    0.0027
     4        1.3093             nan     0.0100    0.0027
     5        1.3036             nan     0.0100    0.0026
     6        1.2989             nan     0.0100    0.0026
     7        1.2945             nan     0.0100    0.0025
     8        1.2905             nan     0.0100    0.0024
     9        1.2858             nan     0.0100    0.0024
    10        1.2804             nan     0.0100    0.0022
    20        1.2384             nan     0.0100    0.0020
    40        1.1729             nan     0.0100    0.0013
    60        1.1273             nan     0.0100    0.0010
    80        1.0898             nan     0.0100    0.0008
   100        1.0602             nan     0.0100    0.0006
   120        1.0353             nan     0.0100    0.0005
   140        1.0154             nan     0.0100    0.0005
   160        0.9974             nan     0.0100    0.0003
   180        0.9821             nan     0.0100    0.0002
   200        0.9689             nan     0.0100    0.0002
   220        0.9567             nan     0.0100    0.0002
   240        0.9456             nan     0.0100    0.0001
   260        0.9358             nan     0.0100    0.0000
   280        0.9271             nan     0.0100   -0.0001
   300        0.9190             nan     0.0100    0.0002
   320        0.9113             nan     0.0100    0.0001
   340        0.9042             nan     0.0100    0.0000
   360        0.8981             nan     0.0100   -0.0000
   380        0.8923             nan     0.0100   -0.0000
   400        0.8866             nan     0.0100    0.0001
   420        0.8818             nan     0.0100   -0.0000
   440        0.8776             nan     0.0100    0.0000
   460        0.8727             nan     0.0100    0.0000
   480        0.8682             nan     0.0100    0.0001
   500        0.8641             nan     0.0100   -0.0000
   520        0.8601             nan     0.0100    0.0001
   540        0.8567             nan     0.0100   -0.0000
   560        0.8532             nan     0.0100    0.0000
   580        0.8502             nan     0.0100    0.0000
   600        0.8466             nan     0.0100    0.0000
   620        0.8440             nan     0.0100    0.0000
   640        0.8410             nan     0.0100   -0.0001
   660        0.8382             nan     0.0100   -0.0001
   680        0.8355             nan     0.0100   -0.0001
   700        0.8329             nan     0.0100   -0.0001
   720        0.8302             nan     0.0100   -0.0000
   740        0.8277             nan     0.0100   -0.0000
   760        0.8251             nan     0.0100    0.0000
   780        0.8229             nan     0.0100   -0.0000
   800        0.8205             nan     0.0100   -0.0000
   820        0.8184             nan     0.0100   -0.0001
   840        0.8162             nan     0.0100   -0.0001
   860        0.8147             nan     0.0100   -0.0002
   880        0.8127             nan     0.0100   -0.0001
   900        0.8111             nan     0.0100   -0.0001
   920        0.8092             nan     0.0100    0.0000
   940        0.8076             nan     0.0100   -0.0000
   960        0.8062             nan     0.0100   -0.0001
   980        0.8046             nan     0.0100   -0.0000
  1000        0.8029             nan     0.0100   -0.0000
  1020        0.8013             nan     0.0100   -0.0001
  1040        0.7996             nan     0.0100   -0.0001
  1060        0.7982             nan     0.0100    0.0000
  1080        0.7968             nan     0.0100   -0.0000
  1100        0.7956             nan     0.0100   -0.0001

- Fold03.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3249             nan     0.0100    0.0036
     2        1.3180             nan     0.0100    0.0035
     3        1.3107             nan     0.0100    0.0035
     4        1.3031             nan     0.0100    0.0032
     5        1.2963             nan     0.0100    0.0036
     6        1.2894             nan     0.0100    0.0031
     7        1.2827             nan     0.0100    0.0032
     8        1.2762             nan     0.0100    0.0032
     9        1.2701             nan     0.0100    0.0027
    10        1.2638             nan     0.0100    0.0029
    20        1.2079             nan     0.0100    0.0026
    40        1.1237             nan     0.0100    0.0019
    60        1.0619             nan     0.0100    0.0013
    80        1.0148             nan     0.0100    0.0009
   100        0.9771             nan     0.0100    0.0007
   120        0.9489             nan     0.0100    0.0006
   140        0.9262             nan     0.0100    0.0004
   160        0.9072             nan     0.0100    0.0003
   180        0.8917             nan     0.0100    0.0002
   200        0.8786             nan     0.0100    0.0002
   220        0.8662             nan     0.0100    0.0002
   240        0.8553             nan     0.0100    0.0003
   260        0.8463             nan     0.0100    0.0002
   280        0.8378             nan     0.0100    0.0000
   300        0.8305             nan     0.0100    0.0000
   320        0.8227             nan     0.0100    0.0000
   340        0.8160             nan     0.0100    0.0000
   360        0.8097             nan     0.0100    0.0001
   380        0.8044             nan     0.0100   -0.0001
   400        0.7992             nan     0.0100   -0.0001
   420        0.7940             nan     0.0100   -0.0001
   440        0.7894             nan     0.0100   -0.0000
   460        0.7848             nan     0.0100    0.0000
   480        0.7807             nan     0.0100   -0.0000
   500        0.7769             nan     0.0100   -0.0001
   520        0.7733             nan     0.0100   -0.0001
   540        0.7697             nan     0.0100    0.0000
   560        0.7662             nan     0.0100   -0.0002
   580        0.7630             nan     0.0100   -0.0000
   600        0.7600             nan     0.0100   -0.0000
   620        0.7568             nan     0.0100    0.0000
   640        0.7540             nan     0.0100   -0.0000
   660        0.7510             nan     0.0100   -0.0001
   680        0.7480             nan     0.0100   -0.0001
   700        0.7451             nan     0.0100   -0.0000
   720        0.7424             nan     0.0100   -0.0001
   740        0.7400             nan     0.0100   -0.0000
   760        0.7371             nan     0.0100   -0.0000
   780        0.7350             nan     0.0100   -0.0001
   800        0.7325             nan     0.0100   -0.0000
   820        0.7299             nan     0.0100   -0.0000
   840        0.7275             nan     0.0100   -0.0001
   860        0.7254             nan     0.0100   -0.0001
   880        0.7228             nan     0.0100   -0.0000
   900        0.7207             nan     0.0100   -0.0001
   920        0.7184             nan     0.0100   -0.0001
   940        0.7162             nan     0.0100   -0.0001
   960        0.7137             nan     0.0100   -0.0001
   980        0.7117             nan     0.0100   -0.0002
  1000        0.7092             nan     0.0100   -0.0001
  1020        0.7068             nan     0.0100   -0.0001
  1040        0.7048             nan     0.0100   -0.0001
  1060        0.7030             nan     0.0100   -0.0001
  1080        0.7014             nan     0.0100   -0.0001
  1100        0.6995             nan     0.0100   -0.0001

- Fold03.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3236             nan     0.0100    0.0035
     2        1.3155             nan     0.0100    0.0039
     3        1.3079             nan     0.0100    0.0038
     4        1.3006             nan     0.0100    0.0037
     5        1.2929             nan     0.0100    0.0037
     6        1.2855             nan     0.0100    0.0034
     7        1.2779             nan     0.0100    0.0037
     8        1.2707             nan     0.0100    0.0033
     9        1.2638             nan     0.0100    0.0033
    10        1.2567             nan     0.0100    0.0033
    20        1.1948             nan     0.0100    0.0026
    40        1.0992             nan     0.0100    0.0019
    60        1.0289             nan     0.0100    0.0014
    80        0.9787             nan     0.0100    0.0010
   100        0.9394             nan     0.0100    0.0008
   120        0.9115             nan     0.0100    0.0003
   140        0.8879             nan     0.0100    0.0006
   160        0.8681             nan     0.0100    0.0002
   180        0.8523             nan     0.0100    0.0001
   200        0.8383             nan     0.0100    0.0002
   220        0.8255             nan     0.0100    0.0003
   240        0.8143             nan     0.0100   -0.0000
   260        0.8036             nan     0.0100   -0.0000
   280        0.7943             nan     0.0100    0.0002
   300        0.7862             nan     0.0100   -0.0000
   320        0.7788             nan     0.0100    0.0000
   340        0.7718             nan     0.0100   -0.0001
   360        0.7659             nan     0.0100   -0.0001
   380        0.7597             nan     0.0100   -0.0002
   400        0.7540             nan     0.0100    0.0000
   420        0.7483             nan     0.0100   -0.0001
   440        0.7432             nan     0.0100    0.0000
   460        0.7386             nan     0.0100   -0.0001
   480        0.7344             nan     0.0100   -0.0001
   500        0.7301             nan     0.0100   -0.0003
   520        0.7260             nan     0.0100   -0.0001
   540        0.7223             nan     0.0100   -0.0002
   560        0.7184             nan     0.0100   -0.0002
   580        0.7142             nan     0.0100   -0.0001
   600        0.7106             nan     0.0100   -0.0001
   620        0.7070             nan     0.0100   -0.0001
   640        0.7034             nan     0.0100   -0.0001
   660        0.6995             nan     0.0100    0.0001
   680        0.6959             nan     0.0100   -0.0002
   700        0.6923             nan     0.0100   -0.0001
   720        0.6890             nan     0.0100   -0.0000
   740        0.6855             nan     0.0100   -0.0001
   760        0.6824             nan     0.0100   -0.0002
   780        0.6792             nan     0.0100   -0.0001
   800        0.6762             nan     0.0100   -0.0001
   820        0.6734             nan     0.0100   -0.0001
   840        0.6699             nan     0.0100   -0.0000
   860        0.6670             nan     0.0100   -0.0001
   880        0.6645             nan     0.0100   -0.0001
   900        0.6614             nan     0.0100   -0.0000
   920        0.6587             nan     0.0100   -0.0001
   940        0.6562             nan     0.0100   -0.0001
   960        0.6532             nan     0.0100   -0.0001
   980        0.6513             nan     0.0100   -0.0001
  1000        0.6490             nan     0.0100   -0.0001
  1020        0.6462             nan     0.0100   -0.0001
  1040        0.6437             nan     0.0100   -0.0002
  1060        0.6408             nan     0.0100   -0.0001
  1080        0.6382             nan     0.0100   -0.0002
  1100        0.6357             nan     0.0100   -0.0001

- Fold03.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2831             nan     0.1000    0.0271
     2        1.2389             nan     0.1000    0.0223
     3        1.1998             nan     0.1000    0.0192
     4        1.1725             nan     0.1000    0.0143
     5        1.1441             nan     0.1000    0.0131
     6        1.1211             nan     0.1000    0.0106
     7        1.1030             nan     0.1000    0.0084
     8        1.0884             nan     0.1000    0.0070
     9        1.0697             nan     0.1000    0.0081
    10        1.0576             nan     0.1000    0.0051
    20        0.9660             nan     0.1000    0.0030
    40        0.8868             nan     0.1000   -0.0000
    60        0.8445             nan     0.1000    0.0007
    80        0.8203             nan     0.1000   -0.0004
   100        0.8010             nan     0.1000   -0.0002
   120        0.7892             nan     0.1000   -0.0006
   140        0.7786             nan     0.1000   -0.0005
   160        0.7718             nan     0.1000   -0.0009
   180        0.7665             nan     0.1000   -0.0018
   200        0.7600             nan     0.1000   -0.0007
   220        0.7536             nan     0.1000   -0.0007
   240        0.7485             nan     0.1000   -0.0002
   260        0.7450             nan     0.1000   -0.0004
   280        0.7393             nan     0.1000   -0.0008
   300        0.7364             nan     0.1000   -0.0007
   320        0.7321             nan     0.1000   -0.0004
   340        0.7277             nan     0.1000   -0.0013
   360        0.7240             nan     0.1000   -0.0011
   380        0.7217             nan     0.1000   -0.0013
   400        0.7168             nan     0.1000   -0.0016
   420        0.7154             nan     0.1000   -0.0007
   440        0.7128             nan     0.1000   -0.0008
   460        0.7122             nan     0.1000   -0.0005
   480        0.7093             nan     0.1000   -0.0011
   500        0.7062             nan     0.1000   -0.0008
   520        0.7043             nan     0.1000   -0.0010
   540        0.7021             nan     0.1000   -0.0005
   560        0.7003             nan     0.1000   -0.0006
   580        0.6998             nan     0.1000   -0.0004
   600        0.6979             nan     0.1000   -0.0013
   620        0.6953             nan     0.1000   -0.0010
   640        0.6933             nan     0.1000   -0.0007
   660        0.6907             nan     0.1000   -0.0007
   680        0.6902             nan     0.1000   -0.0010
   700        0.6883             nan     0.1000   -0.0004
   720        0.6868             nan     0.1000   -0.0006
   740        0.6849             nan     0.1000   -0.0003
   760        0.6828             nan     0.1000   -0.0003
   780        0.6807             nan     0.1000   -0.0006
   800        0.6776             nan     0.1000   -0.0006
   820        0.6760             nan     0.1000   -0.0010
   840        0.6751             nan     0.1000   -0.0008
   860        0.6733             nan     0.1000   -0.0007
   880        0.6720             nan     0.1000   -0.0005
   900        0.6712             nan     0.1000   -0.0007
   920        0.6718             nan     0.1000   -0.0012
   940        0.6693             nan     0.1000   -0.0011
   960        0.6689             nan     0.1000   -0.0010
   980        0.6682             nan     0.1000   -0.0006
  1000        0.6664             nan     0.1000   -0.0009
  1020        0.6654             nan     0.1000   -0.0005
  1040        0.6630             nan     0.1000   -0.0005
  1060        0.6620             nan     0.1000   -0.0010
  1080        0.6610             nan     0.1000   -0.0006
  1100        0.6606             nan     0.1000   -0.0007

- Fold03.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2632             nan     0.1000    0.0347
     2        1.2124             nan     0.1000    0.0250
     3        1.1619             nan     0.1000    0.0225
     4        1.1188             nan     0.1000    0.0219
     5        1.0840             nan     0.1000    0.0177
     6        1.0517             nan     0.1000    0.0153
     7        1.0278             nan     0.1000    0.0116
     8        1.0087             nan     0.1000    0.0097
     9        0.9889             nan     0.1000    0.0100
    10        0.9717             nan     0.1000    0.0079
    20        0.8776             nan     0.1000    0.0023
    40        0.8003             nan     0.1000   -0.0002
    60        0.7632             nan     0.1000   -0.0017
    80        0.7398             nan     0.1000    0.0001
   100        0.7165             nan     0.1000   -0.0008
   120        0.6999             nan     0.1000   -0.0014
   140        0.6825             nan     0.1000   -0.0015
   160        0.6690             nan     0.1000   -0.0013
   180        0.6571             nan     0.1000   -0.0005
   200        0.6477             nan     0.1000   -0.0011
   220        0.6390             nan     0.1000   -0.0008
   240        0.6234             nan     0.1000   -0.0010
   260        0.6115             nan     0.1000   -0.0016
   280        0.6029             nan     0.1000   -0.0009
   300        0.5920             nan     0.1000   -0.0009
   320        0.5823             nan     0.1000   -0.0012
   340        0.5733             nan     0.1000   -0.0006
   360        0.5645             nan     0.1000   -0.0010
   380        0.5570             nan     0.1000   -0.0001
   400        0.5502             nan     0.1000   -0.0003
   420        0.5440             nan     0.1000   -0.0007
   440        0.5376             nan     0.1000   -0.0006
   460        0.5304             nan     0.1000   -0.0006
   480        0.5244             nan     0.1000   -0.0004
   500        0.5164             nan     0.1000   -0.0007
   520        0.5083             nan     0.1000   -0.0007
   540        0.5019             nan     0.1000   -0.0008
   560        0.4978             nan     0.1000   -0.0007
   580        0.4927             nan     0.1000   -0.0006
   600        0.4891             nan     0.1000   -0.0014
   620        0.4846             nan     0.1000   -0.0007
   640        0.4793             nan     0.1000   -0.0015
   660        0.4748             nan     0.1000   -0.0016
   680        0.4704             nan     0.1000   -0.0009
   700        0.4648             nan     0.1000   -0.0006
   720        0.4622             nan     0.1000   -0.0008
   740        0.4568             nan     0.1000   -0.0008
   760        0.4534             nan     0.1000   -0.0006
   780        0.4484             nan     0.1000   -0.0008
   800        0.4455             nan     0.1000   -0.0009
   820        0.4410             nan     0.1000   -0.0003
   840        0.4370             nan     0.1000   -0.0004
   860        0.4334             nan     0.1000   -0.0007
   880        0.4302             nan     0.1000   -0.0007
   900        0.4278             nan     0.1000   -0.0007
   920        0.4235             nan     0.1000   -0.0008
   940        0.4202             nan     0.1000   -0.0003
   960        0.4175             nan     0.1000   -0.0010
   980        0.4145             nan     0.1000   -0.0012
  1000        0.4102             nan     0.1000   -0.0005
  1020        0.4067             nan     0.1000   -0.0005
  1040        0.4050             nan     0.1000   -0.0013
  1060        0.4014             nan     0.1000   -0.0006
  1080        0.3984             nan     0.1000   -0.0004
  1100        0.3947             nan     0.1000   -0.0010

- Fold03.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2541             nan     0.1000    0.0371
     2        1.1949             nan     0.1000    0.0295
     3        1.1434             nan     0.1000    0.0244
     4        1.0964             nan     0.1000    0.0221
     5        1.0615             nan     0.1000    0.0181
     6        1.0256             nan     0.1000    0.0165
     7        0.9941             nan     0.1000    0.0133
     8        0.9683             nan     0.1000    0.0119
     9        0.9492             nan     0.1000    0.0090
    10        0.9388             nan     0.1000    0.0024
    20        0.8398             nan     0.1000    0.0012
    40        0.7561             nan     0.1000   -0.0009
    60        0.7115             nan     0.1000   -0.0014
    80        0.6755             nan     0.1000   -0.0013
   100        0.6483             nan     0.1000   -0.0007
   120        0.6250             nan     0.1000   -0.0026
   140        0.6062             nan     0.1000   -0.0009
   160        0.5914             nan     0.1000   -0.0017
   180        0.5743             nan     0.1000   -0.0014
   200        0.5579             nan     0.1000   -0.0009
   220        0.5395             nan     0.1000   -0.0008
   240        0.5269             nan     0.1000   -0.0012
   260        0.5130             nan     0.1000   -0.0009
   280        0.4993             nan     0.1000   -0.0015
   300        0.4876             nan     0.1000   -0.0013
   320        0.4770             nan     0.1000   -0.0009
   340        0.4690             nan     0.1000   -0.0011
   360        0.4610             nan     0.1000   -0.0015
   380        0.4507             nan     0.1000   -0.0018
   400        0.4416             nan     0.1000   -0.0010
   420        0.4333             nan     0.1000   -0.0012
   440        0.4237             nan     0.1000   -0.0014
   460        0.4166             nan     0.1000   -0.0012
   480        0.4084             nan     0.1000   -0.0010
   500        0.4018             nan     0.1000   -0.0018
   520        0.3943             nan     0.1000   -0.0009
   540        0.3868             nan     0.1000   -0.0011
   560        0.3812             nan     0.1000   -0.0004
   580        0.3735             nan     0.1000   -0.0009
   600        0.3682             nan     0.1000   -0.0005
   620        0.3617             nan     0.1000   -0.0011
   640        0.3560             nan     0.1000   -0.0009
   660        0.3524             nan     0.1000   -0.0011
   680        0.3459             nan     0.1000   -0.0006
   700        0.3404             nan     0.1000   -0.0013
   720        0.3356             nan     0.1000   -0.0008
   740        0.3310             nan     0.1000   -0.0008
   760        0.3251             nan     0.1000   -0.0008
   780        0.3199             nan     0.1000   -0.0009
   800        0.3153             nan     0.1000   -0.0008
   820        0.3125             nan     0.1000   -0.0012
   840        0.3076             nan     0.1000   -0.0013
   860        0.3038             nan     0.1000   -0.0011
   880        0.3002             nan     0.1000   -0.0009
   900        0.2958             nan     0.1000   -0.0007
   920        0.2927             nan     0.1000   -0.0009
   940        0.2896             nan     0.1000   -0.0010
   960        0.2862             nan     0.1000   -0.0005
   980        0.2820             nan     0.1000   -0.0015
  1000        0.2792             nan     0.1000   -0.0013
  1020        0.2748             nan     0.1000   -0.0013
  1040        0.2708             nan     0.1000   -0.0008
  1060        0.2677             nan     0.1000   -0.0011
  1080        0.2656             nan     0.1000   -0.0007
  1100        0.2626             nan     0.1000   -0.0010

- Fold03.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3260             nan     0.0100    0.0032
     2        1.3203             nan     0.0100    0.0030
     3        1.3142             nan     0.0100    0.0030
     4        1.3080             nan     0.0100    0.0029
     5        1.3018             nan     0.0100    0.0028
     6        1.2958             nan     0.0100    0.0027
     7        1.2899             nan     0.0100    0.0027
     8        1.2839             nan     0.0100    0.0027
     9        1.2787             nan     0.0100    0.0027
    10        1.2732             nan     0.0100    0.0026
    20        1.2271             nan     0.0100    0.0022
    40        1.1519             nan     0.0100    0.0015
    60        1.1019             nan     0.0100    0.0008
    80        1.0647             nan     0.0100    0.0008
   100        1.0340             nan     0.0100    0.0007
   120        1.0082             nan     0.0100    0.0005
   140        0.9869             nan     0.0100    0.0004
   160        0.9691             nan     0.0100    0.0002
   180        0.9536             nan     0.0100    0.0000
   200        0.9396             nan     0.0100    0.0003
   220        0.9276             nan     0.0100    0.0002
   240        0.9164             nan     0.0100    0.0002
   260        0.9060             nan     0.0100    0.0002
   280        0.8973             nan     0.0100    0.0002
   300        0.8894             nan     0.0100    0.0002
   320        0.8825             nan     0.0100    0.0001
   340        0.8754             nan     0.0100    0.0001
   360        0.8694             nan     0.0100    0.0002
   380        0.8639             nan     0.0100   -0.0000
   400        0.8588             nan     0.0100    0.0000
   420        0.8541             nan     0.0100   -0.0001
   440        0.8494             nan     0.0100    0.0000
   460        0.8449             nan     0.0100    0.0001
   480        0.8407             nan     0.0100    0.0000
   500        0.8370             nan     0.0100   -0.0002
   520        0.8333             nan     0.0100    0.0000
   540        0.8296             nan     0.0100   -0.0000
   560        0.8261             nan     0.0100    0.0000
   580        0.8230             nan     0.0100    0.0000
   600        0.8201             nan     0.0100    0.0000
   620        0.8171             nan     0.0100    0.0000
   640        0.8145             nan     0.0100   -0.0000
   660        0.8116             nan     0.0100   -0.0000
   680        0.8087             nan     0.0100   -0.0000
   700        0.8064             nan     0.0100   -0.0000
   720        0.8040             nan     0.0100   -0.0001
   740        0.8015             nan     0.0100    0.0000
   760        0.7992             nan     0.0100   -0.0000
   780        0.7970             nan     0.0100   -0.0000
   800        0.7949             nan     0.0100   -0.0000
   820        0.7927             nan     0.0100    0.0000
   840        0.7908             nan     0.0100   -0.0000
   860        0.7891             nan     0.0100   -0.0001
   880        0.7872             nan     0.0100   -0.0001
   900        0.7852             nan     0.0100   -0.0001
   920        0.7835             nan     0.0100   -0.0000
   940        0.7816             nan     0.0100   -0.0000
   960        0.7800             nan     0.0100   -0.0001
   980        0.7785             nan     0.0100   -0.0001
  1000        0.7767             nan     0.0100   -0.0000
  1020        0.7751             nan     0.0100    0.0000
  1040        0.7735             nan     0.0100   -0.0000
  1060        0.7720             nan     0.0100   -0.0000
  1080        0.7706             nan     0.0100   -0.0000
  1100        0.7692             nan     0.0100   -0.0001

- Fold04.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0036
     2        1.3162             nan     0.0100    0.0039
     3        1.3090             nan     0.0100    0.0036
     4        1.3017             nan     0.0100    0.0035
     5        1.2945             nan     0.0100    0.0037
     6        1.2878             nan     0.0100    0.0035
     7        1.2806             nan     0.0100    0.0034
     8        1.2739             nan     0.0100    0.0034
     9        1.2671             nan     0.0100    0.0033
    10        1.2608             nan     0.0100    0.0031
    20        1.2025             nan     0.0100    0.0027
    40        1.1107             nan     0.0100    0.0018
    60        1.0444             nan     0.0100    0.0014
    80        0.9946             nan     0.0100    0.0010
   100        0.9564             nan     0.0100    0.0006
   120        0.9263             nan     0.0100    0.0003
   140        0.9029             nan     0.0100    0.0005
   160        0.8833             nan     0.0100    0.0002
   180        0.8671             nan     0.0100    0.0002
   200        0.8532             nan     0.0100    0.0001
   220        0.8426             nan     0.0100    0.0001
   240        0.8334             nan     0.0100    0.0001
   260        0.8245             nan     0.0100    0.0001
   280        0.8166             nan     0.0100    0.0001
   300        0.8092             nan     0.0100    0.0000
   320        0.8023             nan     0.0100    0.0001
   340        0.7959             nan     0.0100    0.0001
   360        0.7896             nan     0.0100    0.0000
   380        0.7838             nan     0.0100    0.0001
   400        0.7790             nan     0.0100   -0.0001
   420        0.7738             nan     0.0100    0.0000
   440        0.7694             nan     0.0100    0.0000
   460        0.7650             nan     0.0100   -0.0000
   480        0.7612             nan     0.0100   -0.0001
   500        0.7572             nan     0.0100   -0.0002
   520        0.7533             nan     0.0100   -0.0000
   540        0.7497             nan     0.0100   -0.0000
   560        0.7462             nan     0.0100   -0.0001
   580        0.7431             nan     0.0100   -0.0001
   600        0.7401             nan     0.0100   -0.0000
   620        0.7376             nan     0.0100   -0.0001
   640        0.7345             nan     0.0100    0.0000
   660        0.7321             nan     0.0100   -0.0000
   680        0.7294             nan     0.0100   -0.0002
   700        0.7268             nan     0.0100   -0.0001
   720        0.7241             nan     0.0100   -0.0001
   740        0.7212             nan     0.0100   -0.0001
   760        0.7188             nan     0.0100   -0.0001
   780        0.7164             nan     0.0100   -0.0001
   800        0.7138             nan     0.0100   -0.0000
   820        0.7118             nan     0.0100   -0.0001
   840        0.7096             nan     0.0100   -0.0001
   860        0.7071             nan     0.0100   -0.0001
   880        0.7049             nan     0.0100   -0.0000
   900        0.7028             nan     0.0100   -0.0001
   920        0.7007             nan     0.0100   -0.0001
   940        0.6983             nan     0.0100   -0.0002
   960        0.6965             nan     0.0100   -0.0000
   980        0.6943             nan     0.0100   -0.0001
  1000        0.6922             nan     0.0100   -0.0000
  1020        0.6897             nan     0.0100   -0.0001
  1040        0.6880             nan     0.0100   -0.0000
  1060        0.6862             nan     0.0100   -0.0001
  1080        0.6842             nan     0.0100   -0.0001
  1100        0.6825             nan     0.0100   -0.0001

- Fold04.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3236             nan     0.0100    0.0038
     2        1.3158             nan     0.0100    0.0036
     3        1.3074             nan     0.0100    0.0039
     4        1.2996             nan     0.0100    0.0040
     5        1.2918             nan     0.0100    0.0037
     6        1.2843             nan     0.0100    0.0037
     7        1.2766             nan     0.0100    0.0037
     8        1.2692             nan     0.0100    0.0034
     9        1.2622             nan     0.0100    0.0036
    10        1.2551             nan     0.0100    0.0035
    20        1.1898             nan     0.0100    0.0026
    40        1.0905             nan     0.0100    0.0021
    60        1.0166             nan     0.0100    0.0015
    80        0.9620             nan     0.0100    0.0011
   100        0.9213             nan     0.0100    0.0007
   120        0.8923             nan     0.0100    0.0006
   140        0.8670             nan     0.0100    0.0004
   160        0.8471             nan     0.0100    0.0002
   180        0.8311             nan     0.0100    0.0001
   200        0.8173             nan     0.0100    0.0001
   220        0.8050             nan     0.0100    0.0000
   240        0.7949             nan     0.0100   -0.0000
   260        0.7851             nan     0.0100    0.0001
   280        0.7763             nan     0.0100    0.0001
   300        0.7678             nan     0.0100    0.0002
   320        0.7602             nan     0.0100    0.0002
   340        0.7537             nan     0.0100   -0.0001
   360        0.7471             nan     0.0100    0.0000
   380        0.7411             nan     0.0100   -0.0001
   400        0.7360             nan     0.0100   -0.0000
   420        0.7311             nan     0.0100   -0.0000
   440        0.7256             nan     0.0100   -0.0001
   460        0.7209             nan     0.0100   -0.0001
   480        0.7161             nan     0.0100   -0.0001
   500        0.7116             nan     0.0100   -0.0001
   520        0.7071             nan     0.0100   -0.0001
   540        0.7030             nan     0.0100   -0.0000
   560        0.6987             nan     0.0100   -0.0000
   580        0.6946             nan     0.0100   -0.0001
   600        0.6910             nan     0.0100   -0.0000
   620        0.6867             nan     0.0100   -0.0000
   640        0.6829             nan     0.0100   -0.0002
   660        0.6793             nan     0.0100   -0.0000
   680        0.6763             nan     0.0100   -0.0001
   700        0.6732             nan     0.0100   -0.0001
   720        0.6700             nan     0.0100   -0.0001
   740        0.6671             nan     0.0100   -0.0001
   760        0.6641             nan     0.0100   -0.0001
   780        0.6611             nan     0.0100   -0.0001
   800        0.6581             nan     0.0100   -0.0001
   820        0.6552             nan     0.0100   -0.0001
   840        0.6525             nan     0.0100   -0.0002
   860        0.6498             nan     0.0100   -0.0001
   880        0.6471             nan     0.0100   -0.0002
   900        0.6444             nan     0.0100   -0.0002
   920        0.6416             nan     0.0100   -0.0001
   940        0.6386             nan     0.0100   -0.0001
   960        0.6358             nan     0.0100   -0.0000
   980        0.6331             nan     0.0100   -0.0001
  1000        0.6306             nan     0.0100   -0.0002
  1020        0.6277             nan     0.0100   -0.0001
  1040        0.6248             nan     0.0100   -0.0001
  1060        0.6228             nan     0.0100   -0.0002
  1080        0.6202             nan     0.0100   -0.0002
  1100        0.6178             nan     0.0100   -0.0001

- Fold04.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2744             nan     0.1000    0.0299
     2        1.2218             nan     0.1000    0.0240
     3        1.1791             nan     0.1000    0.0202
     4        1.1433             nan     0.1000    0.0165
     5        1.1190             nan     0.1000    0.0133
     6        1.1009             nan     0.1000    0.0082
     7        1.0791             nan     0.1000    0.0109
     8        1.0593             nan     0.1000    0.0092
     9        1.0424             nan     0.1000    0.0072
    10        1.0282             nan     0.1000    0.0064
    20        0.9353             nan     0.1000    0.0024
    40        0.8548             nan     0.1000    0.0003
    60        0.8185             nan     0.1000    0.0003
    80        0.7954             nan     0.1000   -0.0006
   100        0.7775             nan     0.1000   -0.0003
   120        0.7681             nan     0.1000   -0.0007
   140        0.7563             nan     0.1000   -0.0003
   160        0.7469             nan     0.1000   -0.0003
   180        0.7390             nan     0.1000   -0.0007
   200        0.7338             nan     0.1000   -0.0008
   220        0.7268             nan     0.1000   -0.0011
   240        0.7201             nan     0.1000   -0.0012
   260        0.7152             nan     0.1000   -0.0010
   280        0.7137             nan     0.1000   -0.0006
   300        0.7098             nan     0.1000   -0.0010
   320        0.7050             nan     0.1000   -0.0003
   340        0.7005             nan     0.1000   -0.0011
   360        0.6968             nan     0.1000   -0.0011
   380        0.6928             nan     0.1000   -0.0013
   400        0.6900             nan     0.1000   -0.0008
   420        0.6858             nan     0.1000   -0.0004
   440        0.6831             nan     0.1000   -0.0009
   460        0.6792             nan     0.1000   -0.0004
   480        0.6760             nan     0.1000   -0.0014
   500        0.6740             nan     0.1000   -0.0006
   520        0.6715             nan     0.1000   -0.0009
   540        0.6694             nan     0.1000   -0.0014
   560        0.6679             nan     0.1000   -0.0008
   580        0.6663             nan     0.1000   -0.0010
   600        0.6640             nan     0.1000   -0.0007
   620        0.6611             nan     0.1000   -0.0004
   640        0.6594             nan     0.1000   -0.0005
   660        0.6562             nan     0.1000   -0.0011
   680        0.6543             nan     0.1000   -0.0008
   700        0.6523             nan     0.1000   -0.0006
   720        0.6519             nan     0.1000   -0.0011
   740        0.6502             nan     0.1000   -0.0008
   760        0.6466             nan     0.1000   -0.0005
   780        0.6441             nan     0.1000   -0.0003
   800        0.6432             nan     0.1000   -0.0007
   820        0.6412             nan     0.1000   -0.0016
   840        0.6400             nan     0.1000   -0.0003
   860        0.6379             nan     0.1000   -0.0004
   880        0.6358             nan     0.1000   -0.0013
   900        0.6334             nan     0.1000   -0.0005
   920        0.6321             nan     0.1000   -0.0007
   940        0.6303             nan     0.1000   -0.0009
   960        0.6282             nan     0.1000   -0.0003
   980        0.6265             nan     0.1000   -0.0006
  1000        0.6257             nan     0.1000   -0.0005
  1020        0.6235             nan     0.1000   -0.0004
  1040        0.6216             nan     0.1000   -0.0009
  1060        0.6194             nan     0.1000   -0.0019
  1080        0.6183             nan     0.1000   -0.0009
  1100        0.6165             nan     0.1000   -0.0003

- Fold04.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2643             nan     0.1000    0.0365
     2        1.2028             nan     0.1000    0.0306
     3        1.1541             nan     0.1000    0.0253
     4        1.1120             nan     0.1000    0.0219
     5        1.0730             nan     0.1000    0.0172
     6        1.0413             nan     0.1000    0.0153
     7        1.0146             nan     0.1000    0.0121
     8        0.9915             nan     0.1000    0.0111
     9        0.9718             nan     0.1000    0.0099
    10        0.9546             nan     0.1000    0.0081
    20        0.8513             nan     0.1000    0.0032
    40        0.7799             nan     0.1000   -0.0012
    60        0.7403             nan     0.1000   -0.0015
    80        0.7151             nan     0.1000   -0.0014
   100        0.6940             nan     0.1000   -0.0012
   120        0.6765             nan     0.1000   -0.0014
   140        0.6584             nan     0.1000   -0.0020
   160        0.6418             nan     0.1000   -0.0003
   180        0.6298             nan     0.1000   -0.0009
   200        0.6178             nan     0.1000   -0.0017
   220        0.6034             nan     0.1000   -0.0010
   240        0.5911             nan     0.1000   -0.0006
   260        0.5807             nan     0.1000   -0.0004
   280        0.5713             nan     0.1000   -0.0016
   300        0.5602             nan     0.1000   -0.0011
   320        0.5516             nan     0.1000   -0.0005
   340        0.5439             nan     0.1000   -0.0005
   360        0.5369             nan     0.1000   -0.0013
   380        0.5306             nan     0.1000   -0.0010
   400        0.5234             nan     0.1000   -0.0015
   420        0.5180             nan     0.1000   -0.0010
   440        0.5110             nan     0.1000   -0.0001
   460        0.5039             nan     0.1000   -0.0009
   480        0.4963             nan     0.1000   -0.0002
   500        0.4898             nan     0.1000   -0.0007
   520        0.4856             nan     0.1000   -0.0010
   540        0.4775             nan     0.1000   -0.0007
   560        0.4724             nan     0.1000   -0.0012
   580        0.4652             nan     0.1000   -0.0002
   600        0.4602             nan     0.1000   -0.0008
   620        0.4560             nan     0.1000   -0.0021
   640        0.4494             nan     0.1000   -0.0012
   660        0.4462             nan     0.1000   -0.0011
   680        0.4393             nan     0.1000   -0.0012
   700        0.4355             nan     0.1000   -0.0004
   720        0.4309             nan     0.1000   -0.0007
   740        0.4271             nan     0.1000   -0.0011
   760        0.4231             nan     0.1000   -0.0007
   780        0.4189             nan     0.1000   -0.0008
   800        0.4132             nan     0.1000   -0.0003
   820        0.4102             nan     0.1000   -0.0011
   840        0.4069             nan     0.1000   -0.0011
   860        0.4030             nan     0.1000   -0.0006
   880        0.3986             nan     0.1000   -0.0006
   900        0.3953             nan     0.1000   -0.0004
   920        0.3914             nan     0.1000   -0.0006
   940        0.3874             nan     0.1000   -0.0007
   960        0.3838             nan     0.1000   -0.0008
   980        0.3800             nan     0.1000   -0.0010
  1000        0.3789             nan     0.1000   -0.0010
  1020        0.3761             nan     0.1000   -0.0010
  1040        0.3716             nan     0.1000   -0.0008
  1060        0.3691             nan     0.1000   -0.0012
  1080        0.3672             nan     0.1000   -0.0007
  1100        0.3637             nan     0.1000   -0.0011

- Fold04.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2460             nan     0.1000    0.0371
     2        1.1810             nan     0.1000    0.0320
     3        1.1319             nan     0.1000    0.0241
     4        1.0854             nan     0.1000    0.0185
     5        1.0471             nan     0.1000    0.0190
     6        1.0159             nan     0.1000    0.0166
     7        0.9862             nan     0.1000    0.0139
     8        0.9574             nan     0.1000    0.0117
     9        0.9315             nan     0.1000    0.0096
    10        0.9132             nan     0.1000    0.0070
    20        0.8193             nan     0.1000    0.0011
    40        0.7407             nan     0.1000    0.0007
    60        0.6964             nan     0.1000   -0.0007
    80        0.6584             nan     0.1000   -0.0010
   100        0.6338             nan     0.1000   -0.0010
   120        0.6110             nan     0.1000   -0.0015
   140        0.5897             nan     0.1000   -0.0010
   160        0.5708             nan     0.1000   -0.0008
   180        0.5592             nan     0.1000   -0.0012
   200        0.5467             nan     0.1000   -0.0009
   220        0.5324             nan     0.1000   -0.0018
   240        0.5172             nan     0.1000   -0.0008
   260        0.5004             nan     0.1000   -0.0007
   280        0.4852             nan     0.1000   -0.0013
   300        0.4736             nan     0.1000   -0.0009
   320        0.4619             nan     0.1000   -0.0017
   340        0.4530             nan     0.1000   -0.0013
   360        0.4418             nan     0.1000   -0.0012
   380        0.4338             nan     0.1000   -0.0006
   400        0.4252             nan     0.1000   -0.0008
   420        0.4146             nan     0.1000   -0.0007
   440        0.4054             nan     0.1000   -0.0007
   460        0.3942             nan     0.1000   -0.0013
   480        0.3860             nan     0.1000   -0.0011
   500        0.3776             nan     0.1000   -0.0009
   520        0.3696             nan     0.1000   -0.0009
   540        0.3644             nan     0.1000   -0.0004
   560        0.3579             nan     0.1000   -0.0015
   580        0.3505             nan     0.1000   -0.0017
   600        0.3441             nan     0.1000   -0.0011
   620        0.3397             nan     0.1000   -0.0008
   640        0.3326             nan     0.1000   -0.0010
   660        0.3265             nan     0.1000   -0.0007
   680        0.3220             nan     0.1000   -0.0009
   700        0.3169             nan     0.1000   -0.0008
   720        0.3114             nan     0.1000   -0.0009
   740        0.3061             nan     0.1000   -0.0009
   760        0.3023             nan     0.1000   -0.0008
   780        0.2973             nan     0.1000   -0.0010
   800        0.2919             nan     0.1000   -0.0003
   820        0.2874             nan     0.1000   -0.0008
   840        0.2819             nan     0.1000   -0.0014
   860        0.2761             nan     0.1000   -0.0004
   880        0.2708             nan     0.1000   -0.0007
   900        0.2681             nan     0.1000   -0.0008
   920        0.2629             nan     0.1000   -0.0006
   940        0.2603             nan     0.1000   -0.0014
   960        0.2569             nan     0.1000   -0.0013
   980        0.2528             nan     0.1000   -0.0014
  1000        0.2491             nan     0.1000   -0.0012
  1020        0.2465             nan     0.1000   -0.0008
  1040        0.2437             nan     0.1000   -0.0006
  1060        0.2397             nan     0.1000   -0.0004
  1080        0.2369             nan     0.1000   -0.0008
  1100        0.2329             nan     0.1000   -0.0004

- Fold04.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3253             nan     0.0100    0.0031
     2        1.3198             nan     0.0100    0.0029
     3        1.3140             nan     0.0100    0.0030
     4        1.3081             nan     0.0100    0.0027
     5        1.3029             nan     0.0100    0.0028
     6        1.2974             nan     0.0100    0.0028
     7        1.2915             nan     0.0100    0.0027
     8        1.2864             nan     0.0100    0.0027
     9        1.2812             nan     0.0100    0.0026
    10        1.2761             nan     0.0100    0.0026
    20        1.2285             nan     0.0100    0.0021
    40        1.1566             nan     0.0100    0.0015
    60        1.1087             nan     0.0100    0.0010
    80        1.0710             nan     0.0100    0.0007
   100        1.0396             nan     0.0100    0.0006
   120        1.0140             nan     0.0100    0.0005
   140        0.9921             nan     0.0100    0.0004
   160        0.9726             nan     0.0100    0.0003
   180        0.9571             nan     0.0100    0.0002
   200        0.9433             nan     0.0100    0.0002
   220        0.9314             nan     0.0100    0.0001
   240        0.9204             nan     0.0100    0.0002
   260        0.9104             nan     0.0100   -0.0000
   280        0.9015             nan     0.0100    0.0002
   300        0.8933             nan     0.0100    0.0001
   320        0.8861             nan     0.0100    0.0001
   340        0.8793             nan     0.0100    0.0001
   360        0.8736             nan     0.0100    0.0001
   380        0.8677             nan     0.0100    0.0001
   400        0.8625             nan     0.0100   -0.0001
   420        0.8576             nan     0.0100   -0.0000
   440        0.8527             nan     0.0100   -0.0000
   460        0.8482             nan     0.0100    0.0001
   480        0.8439             nan     0.0100    0.0000
   500        0.8398             nan     0.0100   -0.0000
   520        0.8362             nan     0.0100   -0.0000
   540        0.8325             nan     0.0100   -0.0000
   560        0.8291             nan     0.0100    0.0000
   580        0.8258             nan     0.0100    0.0000
   600        0.8224             nan     0.0100   -0.0000
   620        0.8192             nan     0.0100    0.0000
   640        0.8165             nan     0.0100   -0.0000
   660        0.8137             nan     0.0100   -0.0000
   680        0.8105             nan     0.0100    0.0000
   700        0.8080             nan     0.0100    0.0000
   720        0.8052             nan     0.0100   -0.0000
   740        0.8028             nan     0.0100   -0.0000
   760        0.8006             nan     0.0100   -0.0000
   780        0.7984             nan     0.0100   -0.0000
   800        0.7960             nan     0.0100    0.0000
   820        0.7938             nan     0.0100   -0.0000
   840        0.7916             nan     0.0100   -0.0000
   860        0.7897             nan     0.0100   -0.0001
   880        0.7874             nan     0.0100   -0.0001
   900        0.7857             nan     0.0100   -0.0001
   920        0.7836             nan     0.0100   -0.0000
   940        0.7816             nan     0.0100   -0.0001
   960        0.7799             nan     0.0100   -0.0000
   980        0.7782             nan     0.0100   -0.0000
  1000        0.7764             nan     0.0100    0.0000
  1020        0.7748             nan     0.0100   -0.0000
  1040        0.7733             nan     0.0100   -0.0000
  1060        0.7718             nan     0.0100   -0.0001
  1080        0.7702             nan     0.0100   -0.0000
  1100        0.7687             nan     0.0100   -0.0001

- Fold05.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3242             nan     0.0100    0.0040
     2        1.3166             nan     0.0100    0.0037
     3        1.3091             nan     0.0100    0.0037
     4        1.3019             nan     0.0100    0.0035
     5        1.2945             nan     0.0100    0.0033
     6        1.2876             nan     0.0100    0.0035
     7        1.2805             nan     0.0100    0.0035
     8        1.2738             nan     0.0100    0.0033
     9        1.2672             nan     0.0100    0.0030
    10        1.2607             nan     0.0100    0.0030
    20        1.2016             nan     0.0100    0.0026
    40        1.1132             nan     0.0100    0.0019
    60        1.0480             nan     0.0100    0.0014
    80        0.9979             nan     0.0100    0.0010
   100        0.9608             nan     0.0100    0.0008
   120        0.9308             nan     0.0100    0.0005
   140        0.9069             nan     0.0100    0.0005
   160        0.8876             nan     0.0100    0.0004
   180        0.8707             nan     0.0100    0.0004
   200        0.8568             nan     0.0100    0.0002
   220        0.8456             nan     0.0100   -0.0001
   240        0.8344             nan     0.0100    0.0001
   260        0.8248             nan     0.0100    0.0000
   280        0.8159             nan     0.0100    0.0001
   300        0.8077             nan     0.0100    0.0001
   320        0.8002             nan     0.0100    0.0000
   340        0.7938             nan     0.0100   -0.0000
   360        0.7869             nan     0.0100   -0.0000
   380        0.7810             nan     0.0100    0.0000
   400        0.7759             nan     0.0100    0.0000
   420        0.7709             nan     0.0100   -0.0001
   440        0.7662             nan     0.0100   -0.0001
   460        0.7610             nan     0.0100   -0.0000
   480        0.7567             nan     0.0100   -0.0001
   500        0.7525             nan     0.0100   -0.0001
   520        0.7486             nan     0.0100    0.0000
   540        0.7451             nan     0.0100   -0.0000
   560        0.7414             nan     0.0100   -0.0000
   580        0.7382             nan     0.0100   -0.0000
   600        0.7356             nan     0.0100   -0.0002
   620        0.7324             nan     0.0100   -0.0001
   640        0.7292             nan     0.0100   -0.0001
   660        0.7260             nan     0.0100    0.0000
   680        0.7233             nan     0.0100   -0.0000
   700        0.7209             nan     0.0100    0.0000
   720        0.7183             nan     0.0100   -0.0001
   740        0.7157             nan     0.0100   -0.0001
   760        0.7131             nan     0.0100   -0.0001
   780        0.7105             nan     0.0100   -0.0001
   800        0.7077             nan     0.0100    0.0000
   820        0.7053             nan     0.0100   -0.0001
   840        0.7030             nan     0.0100   -0.0001
   860        0.7012             nan     0.0100   -0.0000
   880        0.6991             nan     0.0100   -0.0001
   900        0.6971             nan     0.0100   -0.0000
   920        0.6946             nan     0.0100   -0.0001
   940        0.6923             nan     0.0100   -0.0000
   960        0.6899             nan     0.0100   -0.0001
   980        0.6882             nan     0.0100   -0.0001
  1000        0.6863             nan     0.0100   -0.0000
  1020        0.6842             nan     0.0100   -0.0001
  1040        0.6822             nan     0.0100   -0.0002
  1060        0.6806             nan     0.0100   -0.0001
  1080        0.6789             nan     0.0100   -0.0002
  1100        0.6770             nan     0.0100   -0.0001

- Fold05.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3245             nan     0.0100    0.0039
     2        1.3167             nan     0.0100    0.0038
     3        1.3085             nan     0.0100    0.0041
     4        1.3006             nan     0.0100    0.0040
     5        1.2932             nan     0.0100    0.0034
     6        1.2858             nan     0.0100    0.0035
     7        1.2786             nan     0.0100    0.0033
     8        1.2714             nan     0.0100    0.0032
     9        1.2640             nan     0.0100    0.0035
    10        1.2570             nan     0.0100    0.0036
    20        1.1941             nan     0.0100    0.0029
    40        1.0950             nan     0.0100    0.0021
    60        1.0225             nan     0.0100    0.0015
    80        0.9683             nan     0.0100    0.0011
   100        0.9269             nan     0.0100    0.0007
   120        0.8949             nan     0.0100    0.0006
   140        0.8689             nan     0.0100    0.0004
   160        0.8486             nan     0.0100    0.0001
   180        0.8315             nan     0.0100    0.0003
   200        0.8171             nan     0.0100    0.0001
   220        0.8041             nan     0.0100    0.0001
   240        0.7919             nan     0.0100    0.0000
   260        0.7825             nan     0.0100   -0.0000
   280        0.7733             nan     0.0100    0.0000
   300        0.7648             nan     0.0100    0.0001
   320        0.7576             nan     0.0100    0.0000
   340        0.7512             nan     0.0100   -0.0000
   360        0.7439             nan     0.0100    0.0000
   380        0.7376             nan     0.0100    0.0000
   400        0.7317             nan     0.0100    0.0001
   420        0.7261             nan     0.0100   -0.0001
   440        0.7214             nan     0.0100   -0.0000
   460        0.7157             nan     0.0100   -0.0001
   480        0.7104             nan     0.0100   -0.0001
   500        0.7055             nan     0.0100    0.0000
   520        0.7013             nan     0.0100    0.0000
   540        0.6970             nan     0.0100   -0.0001
   560        0.6933             nan     0.0100   -0.0001
   580        0.6898             nan     0.0100   -0.0002
   600        0.6858             nan     0.0100    0.0000
   620        0.6814             nan     0.0100    0.0000
   640        0.6779             nan     0.0100   -0.0001
   660        0.6740             nan     0.0100   -0.0002
   680        0.6711             nan     0.0100   -0.0000
   700        0.6679             nan     0.0100   -0.0002
   720        0.6643             nan     0.0100   -0.0001
   740        0.6614             nan     0.0100   -0.0000
   760        0.6584             nan     0.0100   -0.0001
   780        0.6557             nan     0.0100   -0.0001
   800        0.6528             nan     0.0100   -0.0002
   820        0.6503             nan     0.0100   -0.0000
   840        0.6473             nan     0.0100   -0.0003
   860        0.6447             nan     0.0100   -0.0002
   880        0.6419             nan     0.0100   -0.0001
   900        0.6394             nan     0.0100   -0.0000
   920        0.6367             nan     0.0100   -0.0000
   940        0.6338             nan     0.0100   -0.0000
   960        0.6313             nan     0.0100   -0.0002
   980        0.6289             nan     0.0100   -0.0001
  1000        0.6267             nan     0.0100   -0.0001
  1020        0.6243             nan     0.0100   -0.0001
  1040        0.6217             nan     0.0100   -0.0001
  1060        0.6193             nan     0.0100   -0.0002
  1080        0.6173             nan     0.0100   -0.0001
  1100        0.6150             nan     0.0100   -0.0000

- Fold05.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2688             nan     0.1000    0.0293
     2        1.2216             nan     0.1000    0.0234
     3        1.1859             nan     0.1000    0.0189
     4        1.1507             nan     0.1000    0.0160
     5        1.1210             nan     0.1000    0.0131
     6        1.1015             nan     0.1000    0.0072
     7        1.0833             nan     0.1000    0.0073
     8        1.0622             nan     0.1000    0.0095
     9        1.0421             nan     0.1000    0.0082
    10        1.0265             nan     0.1000    0.0067
    20        0.9379             nan     0.1000    0.0010
    40        0.8618             nan     0.1000    0.0010
    60        0.8264             nan     0.1000   -0.0011
    80        0.8021             nan     0.1000   -0.0003
   100        0.7819             nan     0.1000   -0.0003
   120        0.7667             nan     0.1000   -0.0004
   140        0.7546             nan     0.1000   -0.0012
   160        0.7462             nan     0.1000   -0.0012
   180        0.7396             nan     0.1000   -0.0005
   200        0.7333             nan     0.1000   -0.0003
   220        0.7286             nan     0.1000   -0.0001
   240        0.7239             nan     0.1000   -0.0009
   260        0.7186             nan     0.1000   -0.0000
   280        0.7154             nan     0.1000   -0.0008
   300        0.7113             nan     0.1000   -0.0007
   320        0.7069             nan     0.1000   -0.0007
   340        0.7044             nan     0.1000   -0.0007
   360        0.7014             nan     0.1000   -0.0004
   380        0.6971             nan     0.1000   -0.0008
   400        0.6952             nan     0.1000   -0.0009
   420        0.6919             nan     0.1000   -0.0015
   440        0.6876             nan     0.1000   -0.0005
   460        0.6845             nan     0.1000   -0.0007
   480        0.6814             nan     0.1000   -0.0008
   500        0.6800             nan     0.1000   -0.0010
   520        0.6775             nan     0.1000   -0.0004
   540        0.6761             nan     0.1000   -0.0003
   560        0.6742             nan     0.1000   -0.0010
   580        0.6723             nan     0.1000   -0.0010
   600        0.6707             nan     0.1000   -0.0006
   620        0.6685             nan     0.1000   -0.0006
   640        0.6663             nan     0.1000   -0.0013
   660        0.6644             nan     0.1000   -0.0009
   680        0.6611             nan     0.1000   -0.0005
   700        0.6587             nan     0.1000   -0.0004
   720        0.6580             nan     0.1000   -0.0009
   740        0.6562             nan     0.1000   -0.0010
   760        0.6541             nan     0.1000   -0.0006
   780        0.6524             nan     0.1000   -0.0010
   800        0.6520             nan     0.1000   -0.0006
   820        0.6513             nan     0.1000   -0.0007
   840        0.6501             nan     0.1000   -0.0006
   860        0.6483             nan     0.1000   -0.0004
   880        0.6471             nan     0.1000   -0.0009
   900        0.6448             nan     0.1000   -0.0007
   920        0.6434             nan     0.1000   -0.0005
   940        0.6433             nan     0.1000   -0.0010
   960        0.6405             nan     0.1000   -0.0007
   980        0.6396             nan     0.1000   -0.0010
  1000        0.6396             nan     0.1000   -0.0006
  1020        0.6361             nan     0.1000   -0.0008
  1040        0.6340             nan     0.1000   -0.0006
  1060        0.6339             nan     0.1000   -0.0007
  1080        0.6332             nan     0.1000   -0.0006
  1100        0.6325             nan     0.1000   -0.0009

- Fold05.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2592             nan     0.1000    0.0363
     2        1.2003             nan     0.1000    0.0301
     3        1.1497             nan     0.1000    0.0247
     4        1.1088             nan     0.1000    0.0209
     5        1.0732             nan     0.1000    0.0163
     6        1.0435             nan     0.1000    0.0141
     7        1.0171             nan     0.1000    0.0130
     8        0.9948             nan     0.1000    0.0100
     9        0.9737             nan     0.1000    0.0094
    10        0.9561             nan     0.1000    0.0085
    20        0.8538             nan     0.1000    0.0026
    40        0.7744             nan     0.1000   -0.0000
    60        0.7347             nan     0.1000   -0.0010
    80        0.7078             nan     0.1000   -0.0006
   100        0.6851             nan     0.1000   -0.0008
   120        0.6701             nan     0.1000   -0.0006
   140        0.6530             nan     0.1000   -0.0010
   160        0.6356             nan     0.1000   -0.0011
   180        0.6224             nan     0.1000    0.0004
   200        0.6106             nan     0.1000   -0.0008
   220        0.6029             nan     0.1000   -0.0006
   240        0.5925             nan     0.1000   -0.0012
   260        0.5859             nan     0.1000   -0.0009
   280        0.5765             nan     0.1000   -0.0014
   300        0.5645             nan     0.1000   -0.0003
   320        0.5565             nan     0.1000   -0.0009
   340        0.5505             nan     0.1000   -0.0014
   360        0.5404             nan     0.1000   -0.0013
   380        0.5345             nan     0.1000   -0.0016
   400        0.5252             nan     0.1000   -0.0010
   420        0.5205             nan     0.1000   -0.0008
   440        0.5145             nan     0.1000   -0.0009
   460        0.5099             nan     0.1000   -0.0009
   480        0.5066             nan     0.1000   -0.0018
   500        0.4995             nan     0.1000   -0.0010
   520        0.4947             nan     0.1000   -0.0009
   540        0.4886             nan     0.1000   -0.0006
   560        0.4817             nan     0.1000   -0.0011
   580        0.4760             nan     0.1000   -0.0008
   600        0.4715             nan     0.1000   -0.0008
   620        0.4644             nan     0.1000   -0.0007
   640        0.4591             nan     0.1000   -0.0006
   660        0.4529             nan     0.1000   -0.0011
   680        0.4491             nan     0.1000   -0.0007
   700        0.4432             nan     0.1000   -0.0008
   720        0.4385             nan     0.1000   -0.0010
   740        0.4346             nan     0.1000   -0.0006
   760        0.4311             nan     0.1000   -0.0007
   780        0.4285             nan     0.1000   -0.0014
   800        0.4236             nan     0.1000   -0.0006
   820        0.4201             nan     0.1000   -0.0010
   840        0.4161             nan     0.1000   -0.0010
   860        0.4133             nan     0.1000   -0.0008
   880        0.4115             nan     0.1000   -0.0006
   900        0.4070             nan     0.1000   -0.0013
   920        0.4027             nan     0.1000   -0.0007
   940        0.3995             nan     0.1000   -0.0008
   960        0.3942             nan     0.1000   -0.0005
   980        0.3917             nan     0.1000   -0.0011
  1000        0.3898             nan     0.1000   -0.0008
  1020        0.3865             nan     0.1000   -0.0006
  1040        0.3831             nan     0.1000   -0.0001
  1060        0.3803             nan     0.1000   -0.0006
  1080        0.3763             nan     0.1000   -0.0002
  1100        0.3732             nan     0.1000   -0.0012

- Fold05.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2526             nan     0.1000    0.0366
     2        1.1844             nan     0.1000    0.0328
     3        1.1323             nan     0.1000    0.0250
     4        1.0845             nan     0.1000    0.0212
     5        1.0468             nan     0.1000    0.0165
     6        1.0158             nan     0.1000    0.0163
     7        0.9872             nan     0.1000    0.0137
     8        0.9602             nan     0.1000    0.0127
     9        0.9376             nan     0.1000    0.0088
    10        0.9198             nan     0.1000    0.0080
    20        0.8192             nan     0.1000    0.0024
    40        0.7326             nan     0.1000   -0.0006
    60        0.6884             nan     0.1000   -0.0014
    80        0.6607             nan     0.1000   -0.0009
   100        0.6385             nan     0.1000   -0.0015
   120        0.6177             nan     0.1000   -0.0018
   140        0.5945             nan     0.1000   -0.0016
   160        0.5760             nan     0.1000   -0.0011
   180        0.5570             nan     0.1000   -0.0009
   200        0.5442             nan     0.1000   -0.0014
   220        0.5301             nan     0.1000   -0.0011
   240        0.5140             nan     0.1000   -0.0020
   260        0.5005             nan     0.1000   -0.0014
   280        0.4906             nan     0.1000   -0.0016
   300        0.4808             nan     0.1000   -0.0013
   320        0.4684             nan     0.1000   -0.0010
   340        0.4593             nan     0.1000   -0.0003
   360        0.4502             nan     0.1000   -0.0009
   380        0.4402             nan     0.1000   -0.0010
   400        0.4337             nan     0.1000   -0.0017
   420        0.4258             nan     0.1000   -0.0014
   440        0.4158             nan     0.1000   -0.0006
   460        0.4053             nan     0.1000   -0.0006
   480        0.3975             nan     0.1000   -0.0007
   500        0.3894             nan     0.1000   -0.0013
   520        0.3801             nan     0.1000   -0.0013
   540        0.3730             nan     0.1000   -0.0019
   560        0.3661             nan     0.1000   -0.0013
   580        0.3606             nan     0.1000   -0.0009
   600        0.3538             nan     0.1000   -0.0009
   620        0.3483             nan     0.1000   -0.0011
   640        0.3427             nan     0.1000   -0.0007
   660        0.3380             nan     0.1000   -0.0010
   680        0.3325             nan     0.1000   -0.0005
   700        0.3273             nan     0.1000   -0.0015
   720        0.3208             nan     0.1000   -0.0009
   740        0.3148             nan     0.1000   -0.0016
   760        0.3114             nan     0.1000   -0.0011
   780        0.3071             nan     0.1000   -0.0014
   800        0.3017             nan     0.1000   -0.0010
   820        0.2991             nan     0.1000   -0.0012
   840        0.2956             nan     0.1000   -0.0007
   860        0.2917             nan     0.1000   -0.0005
   880        0.2880             nan     0.1000   -0.0007
   900        0.2828             nan     0.1000   -0.0008
   920        0.2792             nan     0.1000   -0.0007
   940        0.2747             nan     0.1000   -0.0012
   960        0.2707             nan     0.1000   -0.0009
   980        0.2685             nan     0.1000   -0.0008
  1000        0.2652             nan     0.1000   -0.0010
  1020        0.2621             nan     0.1000   -0.0005
  1040        0.2580             nan     0.1000   -0.0009
  1060        0.2554             nan     0.1000   -0.0005
  1080        0.2510             nan     0.1000   -0.0007
  1100        0.2488             nan     0.1000   -0.0005

- Fold05.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3254             nan     0.0100    0.0031
     2        1.3191             nan     0.0100    0.0029
     3        1.3132             nan     0.0100    0.0029
     4        1.3079             nan     0.0100    0.0030
     5        1.3015             nan     0.0100    0.0029
     6        1.2954             nan     0.0100    0.0028
     7        1.2897             nan     0.0100    0.0028
     8        1.2844             nan     0.0100    0.0027
     9        1.2786             nan     0.0100    0.0026
    10        1.2731             nan     0.0100    0.0026
    20        1.2244             nan     0.0100    0.0021
    40        1.1528             nan     0.0100    0.0015
    60        1.1017             nan     0.0100    0.0011
    80        1.0647             nan     0.0100    0.0007
   100        1.0339             nan     0.0100    0.0006
   120        1.0079             nan     0.0100    0.0005
   140        0.9873             nan     0.0100    0.0004
   160        0.9692             nan     0.0100    0.0003
   180        0.9545             nan     0.0100    0.0002
   200        0.9410             nan     0.0100    0.0002
   220        0.9292             nan     0.0100    0.0002
   240        0.9185             nan     0.0100    0.0002
   260        0.9086             nan     0.0100    0.0002
   280        0.8997             nan     0.0100    0.0001
   300        0.8919             nan     0.0100    0.0001
   320        0.8848             nan     0.0100    0.0001
   340        0.8774             nan     0.0100    0.0001
   360        0.8713             nan     0.0100    0.0001
   380        0.8662             nan     0.0100   -0.0000
   400        0.8610             nan     0.0100    0.0000
   420        0.8558             nan     0.0100    0.0000
   440        0.8511             nan     0.0100    0.0000
   460        0.8465             nan     0.0100    0.0001
   480        0.8421             nan     0.0100    0.0001
   500        0.8383             nan     0.0100   -0.0000
   520        0.8347             nan     0.0100   -0.0000
   540        0.8310             nan     0.0100    0.0000
   560        0.8277             nan     0.0100   -0.0000
   580        0.8244             nan     0.0100    0.0000
   600        0.8214             nan     0.0100    0.0000
   620        0.8185             nan     0.0100    0.0000
   640        0.8158             nan     0.0100    0.0000
   660        0.8135             nan     0.0100    0.0000
   680        0.8115             nan     0.0100   -0.0000
   700        0.8089             nan     0.0100    0.0000
   720        0.8066             nan     0.0100   -0.0001
   740        0.8042             nan     0.0100   -0.0001
   760        0.8020             nan     0.0100   -0.0001
   780        0.8000             nan     0.0100   -0.0000
   800        0.7980             nan     0.0100   -0.0001
   820        0.7961             nan     0.0100   -0.0001
   840        0.7942             nan     0.0100   -0.0000
   860        0.7927             nan     0.0100   -0.0001
   880        0.7910             nan     0.0100   -0.0000
   900        0.7894             nan     0.0100   -0.0000
   920        0.7876             nan     0.0100   -0.0000
   940        0.7860             nan     0.0100   -0.0001
   960        0.7845             nan     0.0100   -0.0001
   980        0.7829             nan     0.0100   -0.0000
  1000        0.7813             nan     0.0100   -0.0001
  1020        0.7797             nan     0.0100   -0.0000
  1040        0.7781             nan     0.0100   -0.0000
  1060        0.7768             nan     0.0100    0.0000
  1080        0.7754             nan     0.0100   -0.0000
  1100        0.7740             nan     0.0100   -0.0000

- Fold06.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3228             nan     0.0100    0.0037
     2        1.3150             nan     0.0100    0.0033
     3        1.3073             nan     0.0100    0.0037
     4        1.3006             nan     0.0100    0.0032
     5        1.2931             nan     0.0100    0.0037
     6        1.2858             nan     0.0100    0.0036
     7        1.2785             nan     0.0100    0.0032
     8        1.2717             nan     0.0100    0.0033
     9        1.2650             nan     0.0100    0.0032
    10        1.2585             nan     0.0100    0.0033
    20        1.1997             nan     0.0100    0.0026
    40        1.1073             nan     0.0100    0.0019
    60        1.0404             nan     0.0100    0.0012
    80        0.9899             nan     0.0100    0.0010
   100        0.9530             nan     0.0100    0.0007
   120        0.9229             nan     0.0100    0.0005
   140        0.9000             nan     0.0100    0.0005
   160        0.8806             nan     0.0100    0.0002
   180        0.8652             nan     0.0100    0.0002
   200        0.8504             nan     0.0100    0.0002
   220        0.8394             nan     0.0100    0.0002
   240        0.8296             nan     0.0100    0.0000
   260        0.8210             nan     0.0100    0.0001
   280        0.8130             nan     0.0100    0.0001
   300        0.8058             nan     0.0100   -0.0000
   320        0.7997             nan     0.0100    0.0000
   340        0.7929             nan     0.0100    0.0000
   360        0.7868             nan     0.0100    0.0000
   380        0.7817             nan     0.0100    0.0000
   400        0.7770             nan     0.0100    0.0001
   420        0.7730             nan     0.0100    0.0000
   440        0.7684             nan     0.0100    0.0000
   460        0.7646             nan     0.0100   -0.0000
   480        0.7608             nan     0.0100   -0.0001
   500        0.7572             nan     0.0100    0.0000
   520        0.7540             nan     0.0100   -0.0001
   540        0.7508             nan     0.0100   -0.0000
   560        0.7474             nan     0.0100   -0.0000
   580        0.7440             nan     0.0100   -0.0000
   600        0.7413             nan     0.0100   -0.0001
   620        0.7379             nan     0.0100   -0.0001
   640        0.7351             nan     0.0100   -0.0001
   660        0.7325             nan     0.0100   -0.0001
   680        0.7297             nan     0.0100   -0.0001
   700        0.7267             nan     0.0100   -0.0000
   720        0.7243             nan     0.0100   -0.0001
   740        0.7217             nan     0.0100   -0.0001
   760        0.7194             nan     0.0100   -0.0001
   780        0.7171             nan     0.0100   -0.0001
   800        0.7147             nan     0.0100   -0.0002
   820        0.7122             nan     0.0100   -0.0001
   840        0.7098             nan     0.0100   -0.0001
   860        0.7075             nan     0.0100   -0.0001
   880        0.7055             nan     0.0100   -0.0001
   900        0.7034             nan     0.0100   -0.0001
   920        0.7014             nan     0.0100   -0.0001
   940        0.6991             nan     0.0100    0.0000
   960        0.6971             nan     0.0100    0.0000
   980        0.6951             nan     0.0100   -0.0001
  1000        0.6932             nan     0.0100   -0.0000
  1020        0.6914             nan     0.0100   -0.0001
  1040        0.6894             nan     0.0100   -0.0002
  1060        0.6880             nan     0.0100   -0.0000
  1080        0.6860             nan     0.0100   -0.0002
  1100        0.6839             nan     0.0100   -0.0001

- Fold06.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3225             nan     0.0100    0.0041
     2        1.3147             nan     0.0100    0.0040
     3        1.3061             nan     0.0100    0.0040
     4        1.2982             nan     0.0100    0.0038
     5        1.2901             nan     0.0100    0.0036
     6        1.2820             nan     0.0100    0.0037
     7        1.2745             nan     0.0100    0.0036
     8        1.2670             nan     0.0100    0.0032
     9        1.2599             nan     0.0100    0.0035
    10        1.2523             nan     0.0100    0.0036
    20        1.1872             nan     0.0100    0.0030
    40        1.0889             nan     0.0100    0.0018
    60        1.0165             nan     0.0100    0.0015
    80        0.9618             nan     0.0100    0.0011
   100        0.9211             nan     0.0100    0.0006
   120        0.8897             nan     0.0100    0.0005
   140        0.8657             nan     0.0100    0.0004
   160        0.8456             nan     0.0100    0.0004
   180        0.8290             nan     0.0100    0.0002
   200        0.8151             nan     0.0100    0.0002
   220        0.8026             nan     0.0100    0.0001
   240        0.7922             nan     0.0100    0.0001
   260        0.7826             nan     0.0100    0.0002
   280        0.7745             nan     0.0100    0.0001
   300        0.7670             nan     0.0100    0.0000
   320        0.7603             nan     0.0100   -0.0000
   340        0.7537             nan     0.0100   -0.0000
   360        0.7478             nan     0.0100   -0.0001
   380        0.7413             nan     0.0100   -0.0002
   400        0.7355             nan     0.0100   -0.0000
   420        0.7303             nan     0.0100   -0.0000
   440        0.7257             nan     0.0100   -0.0000
   460        0.7209             nan     0.0100   -0.0000
   480        0.7164             nan     0.0100   -0.0001
   500        0.7125             nan     0.0100   -0.0001
   520        0.7082             nan     0.0100   -0.0001
   540        0.7043             nan     0.0100   -0.0002
   560        0.7002             nan     0.0100   -0.0000
   580        0.6971             nan     0.0100   -0.0001
   600        0.6933             nan     0.0100   -0.0001
   620        0.6899             nan     0.0100   -0.0001
   640        0.6861             nan     0.0100   -0.0001
   660        0.6830             nan     0.0100   -0.0000
   680        0.6790             nan     0.0100   -0.0001
   700        0.6755             nan     0.0100   -0.0001
   720        0.6725             nan     0.0100   -0.0001
   740        0.6696             nan     0.0100   -0.0001
   760        0.6670             nan     0.0100   -0.0001
   780        0.6637             nan     0.0100   -0.0001
   800        0.6608             nan     0.0100   -0.0001
   820        0.6573             nan     0.0100   -0.0000
   840        0.6547             nan     0.0100   -0.0001
   860        0.6522             nan     0.0100   -0.0001
   880        0.6488             nan     0.0100   -0.0001
   900        0.6466             nan     0.0100   -0.0002
   920        0.6436             nan     0.0100   -0.0001
   940        0.6407             nan     0.0100   -0.0002
   960        0.6384             nan     0.0100   -0.0001
   980        0.6358             nan     0.0100   -0.0001
  1000        0.6332             nan     0.0100   -0.0001
  1020        0.6310             nan     0.0100   -0.0001
  1040        0.6285             nan     0.0100   -0.0001
  1060        0.6259             nan     0.0100   -0.0001
  1080        0.6235             nan     0.0100   -0.0001
  1100        0.6213             nan     0.0100   -0.0002

- Fold06.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2750             nan     0.1000    0.0310
     2        1.2237             nan     0.1000    0.0235
     3        1.1861             nan     0.1000    0.0202
     4        1.1527             nan     0.1000    0.0168
     5        1.1258             nan     0.1000    0.0138
     6        1.1016             nan     0.1000    0.0121
     7        1.0794             nan     0.1000    0.0096
     8        1.0625             nan     0.1000    0.0076
     9        1.0424             nan     0.1000    0.0068
    10        1.0262             nan     0.1000    0.0056
    20        0.9363             nan     0.1000    0.0019
    40        0.8577             nan     0.1000    0.0010
    60        0.8184             nan     0.1000   -0.0001
    80        0.7937             nan     0.1000    0.0001
   100        0.7780             nan     0.1000   -0.0013
   120        0.7647             nan     0.1000   -0.0004
   140        0.7529             nan     0.1000   -0.0007
   160        0.7444             nan     0.1000   -0.0005
   180        0.7384             nan     0.1000   -0.0003
   200        0.7323             nan     0.1000   -0.0005
   220        0.7255             nan     0.1000   -0.0016
   240        0.7227             nan     0.1000   -0.0013
   260        0.7184             nan     0.1000   -0.0003
   280        0.7143             nan     0.1000   -0.0001
   300        0.7106             nan     0.1000   -0.0007
   320        0.7059             nan     0.1000   -0.0004
   340        0.7030             nan     0.1000   -0.0013
   360        0.6995             nan     0.1000   -0.0004
   380        0.6965             nan     0.1000   -0.0001
   400        0.6937             nan     0.1000   -0.0011
   420        0.6885             nan     0.1000   -0.0010
   440        0.6859             nan     0.1000   -0.0003
   460        0.6842             nan     0.1000   -0.0009
   480        0.6821             nan     0.1000   -0.0006
   500        0.6795             nan     0.1000   -0.0006
   520        0.6772             nan     0.1000   -0.0007
   540        0.6765             nan     0.1000   -0.0007
   560        0.6748             nan     0.1000   -0.0014
   580        0.6731             nan     0.1000   -0.0005
   600        0.6723             nan     0.1000   -0.0021
   620        0.6696             nan     0.1000   -0.0007
   640        0.6673             nan     0.1000   -0.0007
   660        0.6657             nan     0.1000   -0.0005
   680        0.6623             nan     0.1000   -0.0006
   700        0.6605             nan     0.1000   -0.0007
   720        0.6590             nan     0.1000   -0.0006
   740        0.6579             nan     0.1000   -0.0010
   760        0.6553             nan     0.1000   -0.0014
   780        0.6531             nan     0.1000   -0.0015
   800        0.6513             nan     0.1000   -0.0008
   820        0.6497             nan     0.1000   -0.0004
   840        0.6479             nan     0.1000   -0.0008
   860        0.6445             nan     0.1000   -0.0004
   880        0.6428             nan     0.1000   -0.0009
   900        0.6432             nan     0.1000   -0.0008
   920        0.6413             nan     0.1000   -0.0003
   940        0.6397             nan     0.1000   -0.0006
   960        0.6379             nan     0.1000   -0.0007
   980        0.6355             nan     0.1000   -0.0008
  1000        0.6344             nan     0.1000   -0.0005
  1020        0.6344             nan     0.1000   -0.0019
  1040        0.6325             nan     0.1000   -0.0008
  1060        0.6324             nan     0.1000   -0.0013
  1080        0.6314             nan     0.1000   -0.0002
  1100        0.6308             nan     0.1000   -0.0007

- Fold06.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2570             nan     0.1000    0.0354
     2        1.1900             nan     0.1000    0.0320
     3        1.1375             nan     0.1000    0.0258
     4        1.0923             nan     0.1000    0.0209
     5        1.0581             nan     0.1000    0.0162
     6        1.0313             nan     0.1000    0.0142
     7        1.0059             nan     0.1000    0.0124
     8        0.9819             nan     0.1000    0.0106
     9        0.9633             nan     0.1000    0.0096
    10        0.9471             nan     0.1000    0.0082
    20        0.8485             nan     0.1000    0.0027
    40        0.7762             nan     0.1000    0.0009
    60        0.7410             nan     0.1000   -0.0006
    80        0.7140             nan     0.1000   -0.0007
   100        0.6942             nan     0.1000   -0.0012
   120        0.6764             nan     0.1000   -0.0008
   140        0.6573             nan     0.1000   -0.0017
   160        0.6465             nan     0.1000   -0.0011
   180        0.6327             nan     0.1000   -0.0001
   200        0.6246             nan     0.1000   -0.0015
   220        0.6099             nan     0.1000   -0.0011
   240        0.5976             nan     0.1000   -0.0010
   260        0.5871             nan     0.1000   -0.0023
   280        0.5786             nan     0.1000   -0.0005
   300        0.5695             nan     0.1000   -0.0010
   320        0.5625             nan     0.1000   -0.0006
   340        0.5551             nan     0.1000   -0.0012
   360        0.5483             nan     0.1000   -0.0004
   380        0.5403             nan     0.1000   -0.0021
   400        0.5346             nan     0.1000   -0.0006
   420        0.5301             nan     0.1000   -0.0009
   440        0.5239             nan     0.1000   -0.0010
   460        0.5189             nan     0.1000   -0.0005
   480        0.5148             nan     0.1000   -0.0010
   500        0.5054             nan     0.1000   -0.0016
   520        0.4975             nan     0.1000   -0.0011
   540        0.4919             nan     0.1000   -0.0014
   560        0.4869             nan     0.1000   -0.0009
   580        0.4820             nan     0.1000   -0.0005
   600        0.4762             nan     0.1000   -0.0008
   620        0.4712             nan     0.1000   -0.0011
   640        0.4675             nan     0.1000   -0.0009
   660        0.4639             nan     0.1000   -0.0017
   680        0.4592             nan     0.1000   -0.0007
   700        0.4534             nan     0.1000   -0.0008
   720        0.4495             nan     0.1000   -0.0008
   740        0.4451             nan     0.1000   -0.0006
   760        0.4438             nan     0.1000   -0.0009
   780        0.4392             nan     0.1000   -0.0007
   800        0.4350             nan     0.1000   -0.0013
   820        0.4303             nan     0.1000   -0.0007
   840        0.4257             nan     0.1000   -0.0013
   860        0.4237             nan     0.1000   -0.0007
   880        0.4194             nan     0.1000   -0.0008
   900        0.4164             nan     0.1000   -0.0011
   920        0.4127             nan     0.1000   -0.0012
   940        0.4099             nan     0.1000   -0.0006
   960        0.4071             nan     0.1000   -0.0011
   980        0.4040             nan     0.1000   -0.0008
  1000        0.4007             nan     0.1000   -0.0006
  1020        0.3969             nan     0.1000   -0.0010
  1040        0.3949             nan     0.1000   -0.0008
  1060        0.3926             nan     0.1000   -0.0015
  1080        0.3895             nan     0.1000   -0.0015
  1100        0.3865             nan     0.1000   -0.0006

- Fold06.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2514             nan     0.1000    0.0431
     2        1.1840             nan     0.1000    0.0313
     3        1.1300             nan     0.1000    0.0278
     4        1.0863             nan     0.1000    0.0210
     5        1.0469             nan     0.1000    0.0168
     6        1.0100             nan     0.1000    0.0140
     7        0.9814             nan     0.1000    0.0135
     8        0.9536             nan     0.1000    0.0115
     9        0.9327             nan     0.1000    0.0088
    10        0.9149             nan     0.1000    0.0085
    20        0.8125             nan     0.1000    0.0010
    40        0.7408             nan     0.1000    0.0006
    60        0.7019             nan     0.1000   -0.0009
    80        0.6734             nan     0.1000   -0.0008
   100        0.6429             nan     0.1000   -0.0005
   120        0.6148             nan     0.1000   -0.0006
   140        0.5924             nan     0.1000   -0.0011
   160        0.5759             nan     0.1000   -0.0008
   180        0.5589             nan     0.1000   -0.0007
   200        0.5459             nan     0.1000   -0.0004
   220        0.5332             nan     0.1000   -0.0014
   240        0.5194             nan     0.1000   -0.0010
   260        0.5085             nan     0.1000   -0.0010
   280        0.4976             nan     0.1000   -0.0015
   300        0.4855             nan     0.1000   -0.0010
   320        0.4749             nan     0.1000   -0.0013
   340        0.4615             nan     0.1000   -0.0014
   360        0.4504             nan     0.1000   -0.0023
   380        0.4406             nan     0.1000   -0.0004
   400        0.4319             nan     0.1000   -0.0010
   420        0.4232             nan     0.1000   -0.0006
   440        0.4173             nan     0.1000   -0.0010
   460        0.4057             nan     0.1000   -0.0011
   480        0.3982             nan     0.1000   -0.0014
   500        0.3923             nan     0.1000   -0.0009
   520        0.3850             nan     0.1000   -0.0006
   540        0.3775             nan     0.1000   -0.0005
   560        0.3719             nan     0.1000   -0.0016
   580        0.3671             nan     0.1000   -0.0013
   600        0.3592             nan     0.1000   -0.0011
   620        0.3528             nan     0.1000   -0.0008
   640        0.3469             nan     0.1000   -0.0014
   660        0.3409             nan     0.1000   -0.0022
   680        0.3359             nan     0.1000   -0.0015
   700        0.3313             nan     0.1000   -0.0011
   720        0.3276             nan     0.1000   -0.0010
   740        0.3216             nan     0.1000   -0.0014
   760        0.3175             nan     0.1000   -0.0010
   780        0.3135             nan     0.1000   -0.0008
   800        0.3088             nan     0.1000   -0.0007
   820        0.3040             nan     0.1000   -0.0003
   840        0.2992             nan     0.1000   -0.0018
   860        0.2940             nan     0.1000   -0.0008
   880        0.2906             nan     0.1000   -0.0013
   900        0.2869             nan     0.1000   -0.0007
   920        0.2829             nan     0.1000   -0.0004
   940        0.2795             nan     0.1000   -0.0005
   960        0.2761             nan     0.1000   -0.0006
   980        0.2729             nan     0.1000   -0.0007
  1000        0.2698             nan     0.1000   -0.0005
  1020        0.2675             nan     0.1000   -0.0014
  1040        0.2629             nan     0.1000   -0.0005
  1060        0.2610             nan     0.1000   -0.0003
  1080        0.2571             nan     0.1000   -0.0006
  1100        0.2549             nan     0.1000   -0.0003

- Fold06.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3266             nan     0.0100    0.0028
     2        1.3206             nan     0.0100    0.0030
     3        1.3141             nan     0.0100    0.0030
     4        1.3088             nan     0.0100    0.0028
     5        1.3031             nan     0.0100    0.0028
     6        1.2979             nan     0.0100    0.0027
     7        1.2928             nan     0.0100    0.0027
     8        1.2876             nan     0.0100    0.0026
     9        1.2826             nan     0.0100    0.0024
    10        1.2774             nan     0.0100    0.0025
    20        1.2321             nan     0.0100    0.0020
    40        1.1623             nan     0.0100    0.0015
    60        1.1135             nan     0.0100    0.0010
    80        1.0762             nan     0.0100    0.0008
   100        1.0454             nan     0.0100    0.0006
   120        1.0200             nan     0.0100    0.0005
   140        0.9998             nan     0.0100    0.0004
   160        0.9814             nan     0.0100    0.0004
   180        0.9659             nan     0.0100    0.0003
   200        0.9521             nan     0.0100    0.0003
   220        0.9407             nan     0.0100    0.0002
   240        0.9305             nan     0.0100    0.0001
   260        0.9214             nan     0.0100    0.0002
   280        0.9133             nan     0.0100    0.0000
   300        0.9051             nan     0.0100    0.0001
   320        0.8981             nan     0.0100    0.0001
   340        0.8918             nan     0.0100    0.0000
   360        0.8858             nan     0.0100    0.0001
   380        0.8807             nan     0.0100    0.0001
   400        0.8755             nan     0.0100    0.0000
   420        0.8707             nan     0.0100    0.0000
   440        0.8662             nan     0.0100   -0.0000
   460        0.8618             nan     0.0100   -0.0000
   480        0.8576             nan     0.0100    0.0000
   500        0.8538             nan     0.0100    0.0000
   520        0.8502             nan     0.0100    0.0000
   540        0.8469             nan     0.0100   -0.0000
   560        0.8435             nan     0.0100   -0.0000
   580        0.8405             nan     0.0100   -0.0000
   600        0.8374             nan     0.0100    0.0000
   620        0.8348             nan     0.0100    0.0000
   640        0.8321             nan     0.0100   -0.0000
   660        0.8294             nan     0.0100    0.0000
   680        0.8270             nan     0.0100   -0.0000
   700        0.8247             nan     0.0100   -0.0000
   720        0.8222             nan     0.0100   -0.0000
   740        0.8200             nan     0.0100    0.0000
   760        0.8177             nan     0.0100    0.0000
   780        0.8157             nan     0.0100   -0.0001
   800        0.8137             nan     0.0100   -0.0000
   820        0.8115             nan     0.0100   -0.0001
   840        0.8096             nan     0.0100   -0.0001
   860        0.8079             nan     0.0100   -0.0000
   880        0.8059             nan     0.0100   -0.0000
   900        0.8043             nan     0.0100   -0.0000
   920        0.8028             nan     0.0100    0.0000
   940        0.8010             nan     0.0100   -0.0000
   960        0.7992             nan     0.0100   -0.0001
   980        0.7979             nan     0.0100   -0.0000
  1000        0.7964             nan     0.0100   -0.0000
  1020        0.7949             nan     0.0100   -0.0001
  1040        0.7933             nan     0.0100    0.0000
  1060        0.7919             nan     0.0100    0.0000
  1080        0.7905             nan     0.0100   -0.0000
  1100        0.7892             nan     0.0100   -0.0001

- Fold07.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0036
     2        1.3163             nan     0.0100    0.0035
     3        1.3091             nan     0.0100    0.0033
     4        1.3021             nan     0.0100    0.0037
     5        1.2951             nan     0.0100    0.0032
     6        1.2889             nan     0.0100    0.0032
     7        1.2818             nan     0.0100    0.0030
     8        1.2754             nan     0.0100    0.0030
     9        1.2690             nan     0.0100    0.0033
    10        1.2635             nan     0.0100    0.0027
    20        1.2077             nan     0.0100    0.0024
    40        1.1201             nan     0.0100    0.0019
    60        1.0562             nan     0.0100    0.0013
    80        1.0091             nan     0.0100    0.0009
   100        0.9726             nan     0.0100    0.0008
   120        0.9417             nan     0.0100    0.0005
   140        0.9185             nan     0.0100    0.0005
   160        0.9001             nan     0.0100    0.0004
   180        0.8847             nan     0.0100    0.0003
   200        0.8716             nan     0.0100    0.0001
   220        0.8599             nan     0.0100    0.0001
   240        0.8496             nan     0.0100    0.0002
   260        0.8408             nan     0.0100   -0.0000
   280        0.8327             nan     0.0100    0.0000
   300        0.8256             nan     0.0100   -0.0000
   320        0.8190             nan     0.0100    0.0000
   340        0.8128             nan     0.0100    0.0001
   360        0.8072             nan     0.0100   -0.0001
   380        0.8014             nan     0.0100   -0.0000
   400        0.7962             nan     0.0100   -0.0000
   420        0.7918             nan     0.0100   -0.0000
   440        0.7876             nan     0.0100   -0.0001
   460        0.7834             nan     0.0100    0.0000
   480        0.7797             nan     0.0100   -0.0001
   500        0.7759             nan     0.0100   -0.0000
   520        0.7719             nan     0.0100    0.0000
   540        0.7684             nan     0.0100   -0.0001
   560        0.7649             nan     0.0100   -0.0000
   580        0.7619             nan     0.0100    0.0000
   600        0.7588             nan     0.0100   -0.0001
   620        0.7558             nan     0.0100   -0.0000
   640        0.7528             nan     0.0100   -0.0000
   660        0.7496             nan     0.0100   -0.0000
   680        0.7472             nan     0.0100   -0.0001
   700        0.7448             nan     0.0100   -0.0001
   720        0.7423             nan     0.0100   -0.0001
   740        0.7397             nan     0.0100   -0.0000
   760        0.7373             nan     0.0100   -0.0001
   780        0.7346             nan     0.0100   -0.0001
   800        0.7324             nan     0.0100   -0.0000
   820        0.7301             nan     0.0100   -0.0000
   840        0.7279             nan     0.0100   -0.0001
   860        0.7258             nan     0.0100   -0.0001
   880        0.7239             nan     0.0100   -0.0001
   900        0.7220             nan     0.0100   -0.0000
   920        0.7202             nan     0.0100   -0.0001
   940        0.7186             nan     0.0100   -0.0001
   960        0.7167             nan     0.0100   -0.0001
   980        0.7149             nan     0.0100   -0.0001
  1000        0.7125             nan     0.0100   -0.0001
  1020        0.7106             nan     0.0100   -0.0001
  1040        0.7086             nan     0.0100   -0.0002
  1060        0.7072             nan     0.0100   -0.0001
  1080        0.7052             nan     0.0100   -0.0002
  1100        0.7032             nan     0.0100   -0.0000

- Fold07.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3238             nan     0.0100    0.0041
     2        1.3157             nan     0.0100    0.0041
     3        1.3085             nan     0.0100    0.0037
     4        1.3005             nan     0.0100    0.0038
     5        1.2926             nan     0.0100    0.0040
     6        1.2848             nan     0.0100    0.0034
     7        1.2774             nan     0.0100    0.0035
     8        1.2706             nan     0.0100    0.0035
     9        1.2636             nan     0.0100    0.0032
    10        1.2569             nan     0.0100    0.0035
    20        1.1948             nan     0.0100    0.0026
    40        1.0986             nan     0.0100    0.0019
    60        1.0286             nan     0.0100    0.0013
    80        0.9753             nan     0.0100    0.0010
   100        0.9362             nan     0.0100    0.0006
   120        0.9058             nan     0.0100    0.0005
   140        0.8820             nan     0.0100    0.0006
   160        0.8624             nan     0.0100    0.0001
   180        0.8460             nan     0.0100    0.0004
   200        0.8335             nan     0.0100    0.0001
   220        0.8223             nan     0.0100    0.0000
   240        0.8119             nan     0.0100   -0.0002
   260        0.8022             nan     0.0100    0.0002
   280        0.7930             nan     0.0100    0.0002
   300        0.7853             nan     0.0100   -0.0001
   320        0.7778             nan     0.0100    0.0001
   340        0.7719             nan     0.0100    0.0001
   360        0.7659             nan     0.0100   -0.0001
   380        0.7599             nan     0.0100   -0.0001
   400        0.7544             nan     0.0100   -0.0002
   420        0.7495             nan     0.0100    0.0000
   440        0.7450             nan     0.0100   -0.0001
   460        0.7407             nan     0.0100   -0.0000
   480        0.7366             nan     0.0100   -0.0001
   500        0.7315             nan     0.0100   -0.0000
   520        0.7276             nan     0.0100   -0.0001
   540        0.7234             nan     0.0100   -0.0000
   560        0.7196             nan     0.0100   -0.0000
   580        0.7161             nan     0.0100   -0.0001
   600        0.7120             nan     0.0100   -0.0001
   620        0.7082             nan     0.0100    0.0000
   640        0.7046             nan     0.0100   -0.0000
   660        0.7014             nan     0.0100   -0.0001
   680        0.6979             nan     0.0100   -0.0001
   700        0.6949             nan     0.0100   -0.0001
   720        0.6914             nan     0.0100   -0.0000
   740        0.6882             nan     0.0100   -0.0002
   760        0.6851             nan     0.0100   -0.0001
   780        0.6821             nan     0.0100   -0.0002
   800        0.6795             nan     0.0100   -0.0001
   820        0.6763             nan     0.0100   -0.0000
   840        0.6733             nan     0.0100   -0.0001
   860        0.6702             nan     0.0100    0.0000
   880        0.6682             nan     0.0100   -0.0001
   900        0.6653             nan     0.0100   -0.0001
   920        0.6630             nan     0.0100   -0.0002
   940        0.6604             nan     0.0100   -0.0001
   960        0.6580             nan     0.0100   -0.0001
   980        0.6558             nan     0.0100   -0.0001
  1000        0.6532             nan     0.0100   -0.0001
  1020        0.6506             nan     0.0100   -0.0001
  1040        0.6484             nan     0.0100   -0.0001
  1060        0.6462             nan     0.0100   -0.0001
  1080        0.6439             nan     0.0100   -0.0000
  1100        0.6418             nan     0.0100   -0.0001

- Fold07.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2743             nan     0.1000    0.0287
     2        1.2287             nan     0.1000    0.0237
     3        1.1936             nan     0.1000    0.0189
     4        1.1590             nan     0.1000    0.0143
     5        1.1354             nan     0.1000    0.0124
     6        1.1158             nan     0.1000    0.0084
     7        1.0914             nan     0.1000    0.0108
     8        1.0697             nan     0.1000    0.0083
     9        1.0540             nan     0.1000    0.0075
    10        1.0395             nan     0.1000    0.0066
    20        0.9480             nan     0.1000    0.0021
    40        0.8733             nan     0.1000    0.0011
    60        0.8355             nan     0.1000    0.0002
    80        0.8145             nan     0.1000   -0.0008
   100        0.7987             nan     0.1000   -0.0000
   120        0.7855             nan     0.1000   -0.0005
   140        0.7762             nan     0.1000   -0.0004
   160        0.7664             nan     0.1000   -0.0018
   180        0.7598             nan     0.1000   -0.0012
   200        0.7539             nan     0.1000   -0.0009
   220        0.7471             nan     0.1000   -0.0004
   240        0.7412             nan     0.1000   -0.0007
   260        0.7354             nan     0.1000   -0.0006
   280        0.7308             nan     0.1000   -0.0010
   300        0.7252             nan     0.1000   -0.0010
   320        0.7218             nan     0.1000   -0.0022
   340        0.7180             nan     0.1000   -0.0007
   360        0.7133             nan     0.1000   -0.0003
   380        0.7096             nan     0.1000   -0.0008
   400        0.7066             nan     0.1000   -0.0005
   420        0.7051             nan     0.1000   -0.0009
   440        0.7017             nan     0.1000   -0.0007
   460        0.6986             nan     0.1000   -0.0009
   480        0.6950             nan     0.1000   -0.0020
   500        0.6936             nan     0.1000   -0.0006
   520        0.6905             nan     0.1000   -0.0010
   540        0.6883             nan     0.1000   -0.0004
   560        0.6857             nan     0.1000   -0.0008
   580        0.6833             nan     0.1000   -0.0005
   600        0.6807             nan     0.1000   -0.0005
   620        0.6786             nan     0.1000   -0.0016
   640        0.6765             nan     0.1000   -0.0005
   660        0.6754             nan     0.1000   -0.0014
   680        0.6736             nan     0.1000   -0.0008
   700        0.6718             nan     0.1000   -0.0009
   720        0.6707             nan     0.1000   -0.0016
   740        0.6680             nan     0.1000   -0.0005
   760        0.6663             nan     0.1000   -0.0013
   780        0.6649             nan     0.1000   -0.0003
   800        0.6639             nan     0.1000   -0.0003
   820        0.6623             nan     0.1000   -0.0006
   840        0.6606             nan     0.1000   -0.0007
   860        0.6601             nan     0.1000   -0.0008
   880        0.6579             nan     0.1000   -0.0006
   900        0.6570             nan     0.1000   -0.0010
   920        0.6558             nan     0.1000   -0.0003
   940        0.6543             nan     0.1000   -0.0003
   960        0.6529             nan     0.1000   -0.0005
   980        0.6500             nan     0.1000   -0.0005
  1000        0.6487             nan     0.1000   -0.0004
  1020        0.6472             nan     0.1000   -0.0008
  1040        0.6454             nan     0.1000   -0.0003
  1060        0.6441             nan     0.1000   -0.0009
  1080        0.6424             nan     0.1000   -0.0012
  1100        0.6414             nan     0.1000   -0.0005

- Fold07.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2661             nan     0.1000    0.0357
     2        1.2073             nan     0.1000    0.0293
     3        1.1583             nan     0.1000    0.0228
     4        1.1172             nan     0.1000    0.0218
     5        1.0794             nan     0.1000    0.0166
     6        1.0472             nan     0.1000    0.0125
     7        1.0248             nan     0.1000    0.0101
     8        1.0029             nan     0.1000    0.0099
     9        0.9831             nan     0.1000    0.0081
    10        0.9672             nan     0.1000    0.0064
    20        0.8675             nan     0.1000    0.0027
    40        0.7957             nan     0.1000    0.0013
    60        0.7596             nan     0.1000   -0.0005
    80        0.7362             nan     0.1000   -0.0014
   100        0.7206             nan     0.1000   -0.0011
   120        0.7007             nan     0.1000   -0.0011
   140        0.6844             nan     0.1000   -0.0004
   160        0.6710             nan     0.1000   -0.0007
   180        0.6565             nan     0.1000   -0.0004
   200        0.6464             nan     0.1000   -0.0008
   220        0.6334             nan     0.1000   -0.0010
   240        0.6217             nan     0.1000   -0.0006
   260        0.6100             nan     0.1000   -0.0008
   280        0.6017             nan     0.1000   -0.0014
   300        0.5914             nan     0.1000   -0.0005
   320        0.5788             nan     0.1000    0.0002
   340        0.5695             nan     0.1000   -0.0017
   360        0.5634             nan     0.1000   -0.0010
   380        0.5571             nan     0.1000   -0.0018
   400        0.5498             nan     0.1000   -0.0013
   420        0.5422             nan     0.1000   -0.0004
   440        0.5367             nan     0.1000   -0.0008
   460        0.5320             nan     0.1000   -0.0005
   480        0.5269             nan     0.1000   -0.0020
   500        0.5194             nan     0.1000   -0.0008
   520        0.5120             nan     0.1000   -0.0010
   540        0.5077             nan     0.1000   -0.0007
   560        0.5024             nan     0.1000   -0.0007
   580        0.4973             nan     0.1000   -0.0002
   600        0.4914             nan     0.1000   -0.0012
   620        0.4858             nan     0.1000   -0.0008
   640        0.4818             nan     0.1000   -0.0006
   660        0.4767             nan     0.1000   -0.0013
   680        0.4713             nan     0.1000   -0.0011
   700        0.4664             nan     0.1000   -0.0007
   720        0.4624             nan     0.1000   -0.0012
   740        0.4582             nan     0.1000   -0.0009
   760        0.4529             nan     0.1000   -0.0009
   780        0.4489             nan     0.1000   -0.0017
   800        0.4462             nan     0.1000   -0.0007
   820        0.4443             nan     0.1000   -0.0011
   840        0.4410             nan     0.1000   -0.0008
   860        0.4368             nan     0.1000   -0.0011
   880        0.4329             nan     0.1000   -0.0006
   900        0.4309             nan     0.1000   -0.0009
   920        0.4274             nan     0.1000   -0.0008
   940        0.4233             nan     0.1000   -0.0006
   960        0.4190             nan     0.1000   -0.0006
   980        0.4149             nan     0.1000   -0.0006
  1000        0.4105             nan     0.1000   -0.0012
  1020        0.4064             nan     0.1000   -0.0009
  1040        0.4020             nan     0.1000   -0.0019
  1060        0.4001             nan     0.1000   -0.0004
  1080        0.3963             nan     0.1000   -0.0005
  1100        0.3949             nan     0.1000   -0.0010

- Fold07.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2552             nan     0.1000    0.0407
     2        1.1879             nan     0.1000    0.0323
     3        1.1323             nan     0.1000    0.0262
     4        1.0867             nan     0.1000    0.0217
     5        1.0479             nan     0.1000    0.0165
     6        1.0204             nan     0.1000    0.0127
     7        0.9959             nan     0.1000    0.0124
     8        0.9729             nan     0.1000    0.0095
     9        0.9526             nan     0.1000    0.0073
    10        0.9355             nan     0.1000    0.0057
    20        0.8328             nan     0.1000    0.0016
    40        0.7545             nan     0.1000   -0.0006
    60        0.7135             nan     0.1000   -0.0002
    80        0.6830             nan     0.1000   -0.0021
   100        0.6587             nan     0.1000   -0.0008
   120        0.6381             nan     0.1000   -0.0007
   140        0.6176             nan     0.1000   -0.0013
   160        0.5974             nan     0.1000   -0.0007
   180        0.5842             nan     0.1000   -0.0022
   200        0.5665             nan     0.1000   -0.0021
   220        0.5531             nan     0.1000   -0.0007
   240        0.5389             nan     0.1000   -0.0012
   260        0.5292             nan     0.1000   -0.0014
   280        0.5176             nan     0.1000   -0.0006
   300        0.5054             nan     0.1000   -0.0013
   320        0.4969             nan     0.1000   -0.0011
   340        0.4866             nan     0.1000   -0.0014
   360        0.4781             nan     0.1000   -0.0010
   380        0.4663             nan     0.1000   -0.0013
   400        0.4590             nan     0.1000   -0.0017
   420        0.4488             nan     0.1000   -0.0009
   440        0.4419             nan     0.1000   -0.0013
   460        0.4331             nan     0.1000   -0.0016
   480        0.4249             nan     0.1000   -0.0012
   500        0.4180             nan     0.1000   -0.0003
   520        0.4085             nan     0.1000   -0.0008
   540        0.4028             nan     0.1000   -0.0013
   560        0.3954             nan     0.1000   -0.0009
   580        0.3876             nan     0.1000   -0.0008
   600        0.3824             nan     0.1000   -0.0012
   620        0.3727             nan     0.1000   -0.0011
   640        0.3683             nan     0.1000   -0.0009
   660        0.3621             nan     0.1000   -0.0007
   680        0.3563             nan     0.1000   -0.0006
   700        0.3501             nan     0.1000   -0.0007
   720        0.3453             nan     0.1000   -0.0005
   740        0.3406             nan     0.1000   -0.0008
   760        0.3366             nan     0.1000   -0.0007
   780        0.3331             nan     0.1000   -0.0010
   800        0.3280             nan     0.1000   -0.0007
   820        0.3247             nan     0.1000   -0.0006
   840        0.3208             nan     0.1000   -0.0011
   860        0.3161             nan     0.1000   -0.0014
   880        0.3104             nan     0.1000   -0.0009
   900        0.3053             nan     0.1000   -0.0007
   920        0.3014             nan     0.1000   -0.0008
   940        0.2966             nan     0.1000   -0.0006
   960        0.2927             nan     0.1000   -0.0004
   980        0.2878             nan     0.1000   -0.0008
  1000        0.2848             nan     0.1000   -0.0011
  1020        0.2805             nan     0.1000   -0.0011
  1040        0.2762             nan     0.1000   -0.0013
  1060        0.2738             nan     0.1000   -0.0008
  1080        0.2704             nan     0.1000   -0.0006
  1100        0.2671             nan     0.1000   -0.0005

- Fold07.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3263             nan     0.0100    0.0029
     2        1.3204             nan     0.0100    0.0028
     3        1.3148             nan     0.0100    0.0027
     4        1.3091             nan     0.0100    0.0027
     5        1.3038             nan     0.0100    0.0026
     6        1.2984             nan     0.0100    0.0025
     7        1.2932             nan     0.0100    0.0025
     8        1.2880             nan     0.0100    0.0025
     9        1.2828             nan     0.0100    0.0024
    10        1.2781             nan     0.0100    0.0024
    20        1.2334             nan     0.0100    0.0019
    40        1.1663             nan     0.0100    0.0013
    60        1.1192             nan     0.0100    0.0008
    80        1.0834             nan     0.0100    0.0008
   100        1.0542             nan     0.0100    0.0006
   120        1.0299             nan     0.0100    0.0005
   140        1.0096             nan     0.0100    0.0004
   160        0.9937             nan     0.0100    0.0003
   180        0.9790             nan     0.0100    0.0003
   200        0.9653             nan     0.0100    0.0002
   220        0.9540             nan     0.0100    0.0000
   240        0.9430             nan     0.0100    0.0001
   260        0.9337             nan     0.0100    0.0001
   280        0.9255             nan     0.0100    0.0001
   300        0.9174             nan     0.0100    0.0000
   320        0.9106             nan     0.0100    0.0001
   340        0.9046             nan     0.0100    0.0000
   360        0.8988             nan     0.0100    0.0001
   380        0.8928             nan     0.0100    0.0001
   400        0.8875             nan     0.0100    0.0000
   420        0.8823             nan     0.0100    0.0001
   440        0.8778             nan     0.0100    0.0000
   460        0.8735             nan     0.0100    0.0001
   480        0.8698             nan     0.0100   -0.0000
   500        0.8656             nan     0.0100   -0.0000
   520        0.8621             nan     0.0100    0.0000
   540        0.8585             nan     0.0100    0.0000
   560        0.8551             nan     0.0100    0.0000
   580        0.8518             nan     0.0100   -0.0000
   600        0.8486             nan     0.0100    0.0000
   620        0.8457             nan     0.0100    0.0000
   640        0.8427             nan     0.0100   -0.0000
   660        0.8399             nan     0.0100    0.0000
   680        0.8373             nan     0.0100   -0.0001
   700        0.8348             nan     0.0100   -0.0000
   720        0.8327             nan     0.0100   -0.0000
   740        0.8302             nan     0.0100   -0.0000
   760        0.8279             nan     0.0100   -0.0001
   780        0.8258             nan     0.0100   -0.0000
   800        0.8235             nan     0.0100   -0.0000
   820        0.8217             nan     0.0100   -0.0001
   840        0.8197             nan     0.0100   -0.0001
   860        0.8177             nan     0.0100   -0.0000
   880        0.8158             nan     0.0100   -0.0000
   900        0.8144             nan     0.0100   -0.0001
   920        0.8126             nan     0.0100   -0.0000
   940        0.8110             nan     0.0100   -0.0001
   960        0.8092             nan     0.0100   -0.0000
   980        0.8075             nan     0.0100   -0.0000
  1000        0.8059             nan     0.0100   -0.0000
  1020        0.8045             nan     0.0100    0.0000
  1040        0.8032             nan     0.0100   -0.0001
  1060        0.8019             nan     0.0100   -0.0001
  1080        0.8005             nan     0.0100   -0.0000
  1100        0.7993             nan     0.0100   -0.0000

- Fold08.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3251             nan     0.0100    0.0036
     2        1.3183             nan     0.0100    0.0035
     3        1.3124             nan     0.0100    0.0029
     4        1.3056             nan     0.0100    0.0032
     5        1.2985             nan     0.0100    0.0033
     6        1.2922             nan     0.0100    0.0034
     7        1.2860             nan     0.0100    0.0032
     8        1.2800             nan     0.0100    0.0029
     9        1.2736             nan     0.0100    0.0030
    10        1.2674             nan     0.0100    0.0030
    20        1.2114             nan     0.0100    0.0025
    40        1.1265             nan     0.0100    0.0019
    60        1.0640             nan     0.0100    0.0012
    80        1.0163             nan     0.0100    0.0008
   100        0.9796             nan     0.0100    0.0005
   120        0.9516             nan     0.0100    0.0002
   140        0.9293             nan     0.0100    0.0003
   160        0.9114             nan     0.0100    0.0003
   180        0.8956             nan     0.0100    0.0002
   200        0.8830             nan     0.0100    0.0002
   220        0.8713             nan     0.0100   -0.0000
   240        0.8614             nan     0.0100    0.0001
   260        0.8514             nan     0.0100    0.0002
   280        0.8435             nan     0.0100    0.0001
   300        0.8357             nan     0.0100    0.0001
   320        0.8290             nan     0.0100    0.0000
   340        0.8227             nan     0.0100   -0.0000
   360        0.8171             nan     0.0100   -0.0000
   380        0.8118             nan     0.0100   -0.0000
   400        0.8064             nan     0.0100    0.0000
   420        0.8020             nan     0.0100    0.0000
   440        0.7974             nan     0.0100   -0.0000
   460        0.7925             nan     0.0100   -0.0000
   480        0.7886             nan     0.0100   -0.0001
   500        0.7848             nan     0.0100   -0.0001
   520        0.7809             nan     0.0100   -0.0000
   540        0.7775             nan     0.0100   -0.0000
   560        0.7736             nan     0.0100   -0.0000
   580        0.7700             nan     0.0100   -0.0001
   600        0.7667             nan     0.0100   -0.0001
   620        0.7640             nan     0.0100   -0.0001
   640        0.7611             nan     0.0100   -0.0001
   660        0.7587             nan     0.0100   -0.0000
   680        0.7561             nan     0.0100   -0.0001
   700        0.7536             nan     0.0100   -0.0001
   720        0.7510             nan     0.0100   -0.0001
   740        0.7484             nan     0.0100   -0.0002
   760        0.7455             nan     0.0100   -0.0001
   780        0.7430             nan     0.0100   -0.0001
   800        0.7405             nan     0.0100   -0.0001
   820        0.7383             nan     0.0100   -0.0000
   840        0.7359             nan     0.0100   -0.0001
   860        0.7331             nan     0.0100   -0.0001
   880        0.7309             nan     0.0100   -0.0000
   900        0.7289             nan     0.0100   -0.0001
   920        0.7269             nan     0.0100    0.0001
   940        0.7246             nan     0.0100   -0.0000
   960        0.7232             nan     0.0100   -0.0001
   980        0.7215             nan     0.0100   -0.0001
  1000        0.7197             nan     0.0100   -0.0001
  1020        0.7180             nan     0.0100   -0.0001
  1040        0.7160             nan     0.0100   -0.0001
  1060        0.7143             nan     0.0100   -0.0001
  1080        0.7123             nan     0.0100   -0.0001
  1100        0.7101             nan     0.0100   -0.0000

- Fold08.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3240             nan     0.0100    0.0036
     2        1.3166             nan     0.0100    0.0037
     3        1.3088             nan     0.0100    0.0038
     4        1.3015             nan     0.0100    0.0035
     5        1.2943             nan     0.0100    0.0036
     6        1.2874             nan     0.0100    0.0036
     7        1.2807             nan     0.0100    0.0030
     8        1.2737             nan     0.0100    0.0034
     9        1.2668             nan     0.0100    0.0031
    10        1.2594             nan     0.0100    0.0033
    20        1.1991             nan     0.0100    0.0028
    40        1.1039             nan     0.0100    0.0020
    60        1.0336             nan     0.0100    0.0013
    80        0.9821             nan     0.0100    0.0007
   100        0.9452             nan     0.0100    0.0007
   120        0.9163             nan     0.0100    0.0005
   140        0.8938             nan     0.0100    0.0002
   160        0.8741             nan     0.0100    0.0003
   180        0.8586             nan     0.0100    0.0004
   200        0.8453             nan     0.0100    0.0002
   220        0.8328             nan     0.0100    0.0001
   240        0.8226             nan     0.0100    0.0001
   260        0.8123             nan     0.0100    0.0003
   280        0.8028             nan     0.0100    0.0001
   300        0.7942             nan     0.0100    0.0000
   320        0.7860             nan     0.0100   -0.0001
   340        0.7787             nan     0.0100    0.0001
   360        0.7723             nan     0.0100    0.0000
   380        0.7669             nan     0.0100   -0.0001
   400        0.7613             nan     0.0100   -0.0000
   420        0.7562             nan     0.0100   -0.0001
   440        0.7515             nan     0.0100    0.0000
   460        0.7465             nan     0.0100   -0.0001
   480        0.7419             nan     0.0100   -0.0001
   500        0.7374             nan     0.0100   -0.0001
   520        0.7332             nan     0.0100    0.0000
   540        0.7291             nan     0.0100   -0.0001
   560        0.7250             nan     0.0100   -0.0001
   580        0.7210             nan     0.0100    0.0000
   600        0.7175             nan     0.0100   -0.0000
   620        0.7133             nan     0.0100   -0.0000
   640        0.7098             nan     0.0100   -0.0001
   660        0.7064             nan     0.0100   -0.0001
   680        0.7024             nan     0.0100   -0.0000
   700        0.6992             nan     0.0100   -0.0001
   720        0.6964             nan     0.0100   -0.0001
   740        0.6935             nan     0.0100   -0.0001
   760        0.6907             nan     0.0100   -0.0002
   780        0.6877             nan     0.0100   -0.0000
   800        0.6847             nan     0.0100   -0.0001
   820        0.6818             nan     0.0100   -0.0001
   840        0.6789             nan     0.0100   -0.0001
   860        0.6760             nan     0.0100    0.0000
   880        0.6733             nan     0.0100   -0.0000
   900        0.6709             nan     0.0100   -0.0002
   920        0.6687             nan     0.0100   -0.0001
   940        0.6659             nan     0.0100   -0.0001
   960        0.6631             nan     0.0100   -0.0000
   980        0.6609             nan     0.0100   -0.0001
  1000        0.6587             nan     0.0100   -0.0002
  1020        0.6561             nan     0.0100   -0.0001
  1040        0.6538             nan     0.0100   -0.0002
  1060        0.6508             nan     0.0100   -0.0001
  1080        0.6481             nan     0.0100   -0.0000
  1100        0.6458             nan     0.0100   -0.0000

- Fold08.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2758             nan     0.1000    0.0281
     2        1.2298             nan     0.1000    0.0211
     3        1.1889             nan     0.1000    0.0182
     4        1.1573             nan     0.1000    0.0149
     5        1.1323             nan     0.1000    0.0123
     6        1.1143             nan     0.1000    0.0072
     7        1.0934             nan     0.1000    0.0095
     8        1.0769             nan     0.1000    0.0075
     9        1.0636             nan     0.1000    0.0070
    10        1.0485             nan     0.1000    0.0074
    20        0.9632             nan     0.1000    0.0021
    40        0.8843             nan     0.1000    0.0007
    60        0.8502             nan     0.1000   -0.0002
    80        0.8229             nan     0.1000   -0.0010
   100        0.8040             nan     0.1000   -0.0005
   120        0.7911             nan     0.1000    0.0000
   140        0.7817             nan     0.1000   -0.0006
   160        0.7723             nan     0.1000   -0.0006
   180        0.7650             nan     0.1000   -0.0002
   200        0.7593             nan     0.1000   -0.0003
   220        0.7522             nan     0.1000   -0.0006
   240        0.7468             nan     0.1000   -0.0011
   260        0.7416             nan     0.1000   -0.0000
   280        0.7365             nan     0.1000    0.0000
   300        0.7318             nan     0.1000   -0.0012
   320        0.7294             nan     0.1000   -0.0011
   340        0.7256             nan     0.1000   -0.0007
   360        0.7221             nan     0.1000   -0.0006
   380        0.7203             nan     0.1000   -0.0006
   400        0.7172             nan     0.1000   -0.0002
   420        0.7133             nan     0.1000   -0.0003
   440        0.7105             nan     0.1000   -0.0011
   460        0.7089             nan     0.1000   -0.0015
   480        0.7059             nan     0.1000   -0.0012
   500        0.7023             nan     0.1000   -0.0006
   520        0.6998             nan     0.1000   -0.0004
   540        0.6977             nan     0.1000   -0.0011
   560        0.6955             nan     0.1000   -0.0005
   580        0.6931             nan     0.1000   -0.0008
   600        0.6919             nan     0.1000   -0.0001
   620        0.6898             nan     0.1000   -0.0005
   640        0.6869             nan     0.1000   -0.0004
   660        0.6841             nan     0.1000   -0.0006
   680        0.6833             nan     0.1000   -0.0007
   700        0.6818             nan     0.1000   -0.0012
   720        0.6801             nan     0.1000   -0.0015
   740        0.6793             nan     0.1000   -0.0009
   760        0.6789             nan     0.1000   -0.0010
   780        0.6766             nan     0.1000   -0.0010
   800        0.6737             nan     0.1000   -0.0007
   820        0.6727             nan     0.1000   -0.0010
   840        0.6707             nan     0.1000   -0.0009
   860        0.6689             nan     0.1000   -0.0006
   880        0.6676             nan     0.1000   -0.0005
   900        0.6665             nan     0.1000   -0.0012
   920        0.6642             nan     0.1000   -0.0004
   940        0.6620             nan     0.1000   -0.0009
   960        0.6604             nan     0.1000   -0.0007
   980        0.6594             nan     0.1000   -0.0008
  1000        0.6573             nan     0.1000   -0.0010
  1020        0.6558             nan     0.1000   -0.0009
  1040        0.6553             nan     0.1000   -0.0011
  1060        0.6537             nan     0.1000   -0.0010
  1080        0.6525             nan     0.1000   -0.0005
  1100        0.6516             nan     0.1000   -0.0010

- Fold08.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2651             nan     0.1000    0.0297
     2        1.2046             nan     0.1000    0.0281
     3        1.1562             nan     0.1000    0.0239
     4        1.1209             nan     0.1000    0.0186
     5        1.0814             nan     0.1000    0.0185
     6        1.0544             nan     0.1000    0.0112
     7        1.0271             nan     0.1000    0.0121
     8        1.0046             nan     0.1000    0.0102
     9        0.9877             nan     0.1000    0.0084
    10        0.9723             nan     0.1000    0.0070
    20        0.8788             nan     0.1000    0.0016
    40        0.8048             nan     0.1000   -0.0010
    60        0.7654             nan     0.1000   -0.0004
    80        0.7364             nan     0.1000   -0.0011
   100        0.7187             nan     0.1000   -0.0008
   120        0.7007             nan     0.1000   -0.0008
   140        0.6870             nan     0.1000   -0.0009
   160        0.6748             nan     0.1000   -0.0015
   180        0.6621             nan     0.1000   -0.0003
   200        0.6493             nan     0.1000   -0.0010
   220        0.6360             nan     0.1000   -0.0005
   240        0.6258             nan     0.1000   -0.0017
   260        0.6146             nan     0.1000   -0.0007
   280        0.6036             nan     0.1000   -0.0002
   300        0.5939             nan     0.1000   -0.0011
   320        0.5882             nan     0.1000   -0.0006
   340        0.5780             nan     0.1000   -0.0005
   360        0.5705             nan     0.1000   -0.0007
   380        0.5633             nan     0.1000   -0.0006
   400        0.5566             nan     0.1000   -0.0006
   420        0.5509             nan     0.1000   -0.0006
   440        0.5429             nan     0.1000   -0.0007
   460        0.5350             nan     0.1000   -0.0008
   480        0.5305             nan     0.1000   -0.0010
   500        0.5266             nan     0.1000   -0.0007
   520        0.5196             nan     0.1000   -0.0003
   540        0.5155             nan     0.1000   -0.0007
   560        0.5093             nan     0.1000   -0.0011
   580        0.5025             nan     0.1000   -0.0002
   600        0.4984             nan     0.1000   -0.0008
   620        0.4910             nan     0.1000   -0.0003
   640        0.4858             nan     0.1000   -0.0011
   660        0.4826             nan     0.1000   -0.0010
   680        0.4803             nan     0.1000   -0.0010
   700        0.4780             nan     0.1000   -0.0011
   720        0.4736             nan     0.1000   -0.0003
   740        0.4677             nan     0.1000   -0.0014
   760        0.4632             nan     0.1000   -0.0008
   780        0.4582             nan     0.1000   -0.0012
   800        0.4545             nan     0.1000   -0.0009
   820        0.4517             nan     0.1000   -0.0013
   840        0.4486             nan     0.1000   -0.0012
   860        0.4460             nan     0.1000   -0.0008
   880        0.4414             nan     0.1000   -0.0006
   900        0.4362             nan     0.1000   -0.0013
   920        0.4321             nan     0.1000   -0.0004
   940        0.4294             nan     0.1000   -0.0010
   960        0.4263             nan     0.1000   -0.0015
   980        0.4213             nan     0.1000   -0.0004
  1000        0.4183             nan     0.1000   -0.0010
  1020        0.4150             nan     0.1000   -0.0004
  1040        0.4116             nan     0.1000   -0.0006
  1060        0.4084             nan     0.1000   -0.0004
  1080        0.4053             nan     0.1000   -0.0007
  1100        0.4031             nan     0.1000   -0.0013

- Fold08.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2588             nan     0.1000    0.0362
     2        1.1931             nan     0.1000    0.0318
     3        1.1407             nan     0.1000    0.0238
     4        1.0947             nan     0.1000    0.0228
     5        1.0563             nan     0.1000    0.0188
     6        1.0208             nan     0.1000    0.0131
     7        0.9905             nan     0.1000    0.0107
     8        0.9703             nan     0.1000    0.0088
     9        0.9500             nan     0.1000    0.0087
    10        0.9329             nan     0.1000    0.0076
    20        0.8385             nan     0.1000    0.0006
    40        0.7623             nan     0.1000    0.0014
    60        0.7193             nan     0.1000   -0.0019
    80        0.6883             nan     0.1000   -0.0011
   100        0.6605             nan     0.1000   -0.0010
   120        0.6305             nan     0.1000   -0.0006
   140        0.6119             nan     0.1000   -0.0008
   160        0.5947             nan     0.1000   -0.0011
   180        0.5791             nan     0.1000   -0.0010
   200        0.5589             nan     0.1000   -0.0016
   220        0.5424             nan     0.1000   -0.0011
   240        0.5285             nan     0.1000   -0.0010
   260        0.5164             nan     0.1000   -0.0007
   280        0.5017             nan     0.1000   -0.0009
   300        0.4873             nan     0.1000   -0.0008
   320        0.4769             nan     0.1000   -0.0003
   340        0.4663             nan     0.1000   -0.0008
   360        0.4557             nan     0.1000   -0.0015
   380        0.4469             nan     0.1000   -0.0013
   400        0.4368             nan     0.1000   -0.0010
   420        0.4300             nan     0.1000   -0.0013
   440        0.4220             nan     0.1000   -0.0008
   460        0.4143             nan     0.1000   -0.0010
   480        0.4076             nan     0.1000   -0.0006
   500        0.4005             nan     0.1000   -0.0012
   520        0.3915             nan     0.1000   -0.0010
   540        0.3853             nan     0.1000   -0.0008
   560        0.3781             nan     0.1000   -0.0008
   580        0.3718             nan     0.1000   -0.0007
   600        0.3687             nan     0.1000   -0.0011
   620        0.3608             nan     0.1000   -0.0012
   640        0.3551             nan     0.1000   -0.0011
   660        0.3485             nan     0.1000   -0.0008
   680        0.3432             nan     0.1000   -0.0008
   700        0.3374             nan     0.1000   -0.0004
   720        0.3328             nan     0.1000   -0.0008
   740        0.3295             nan     0.1000   -0.0010
   760        0.3244             nan     0.1000   -0.0003
   780        0.3195             nan     0.1000   -0.0006
   800        0.3153             nan     0.1000   -0.0005
   820        0.3110             nan     0.1000   -0.0011
   840        0.3053             nan     0.1000   -0.0010
   860        0.3007             nan     0.1000   -0.0007
   880        0.2966             nan     0.1000   -0.0006
   900        0.2916             nan     0.1000   -0.0013
   920        0.2890             nan     0.1000   -0.0009
   940        0.2860             nan     0.1000   -0.0011
   960        0.2806             nan     0.1000   -0.0010
   980        0.2780             nan     0.1000   -0.0015
  1000        0.2748             nan     0.1000   -0.0008
  1020        0.2708             nan     0.1000   -0.0011
  1040        0.2663             nan     0.1000   -0.0011
  1060        0.2634             nan     0.1000   -0.0006
  1080        0.2601             nan     0.1000   -0.0004
  1100        0.2573             nan     0.1000   -0.0012

- Fold08.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3260             nan     0.0100    0.0030
     2        1.3202             nan     0.0100    0.0029
     3        1.3147             nan     0.0100    0.0028
     4        1.3092             nan     0.0100    0.0028
     5        1.3038             nan     0.0100    0.0027
     6        1.2985             nan     0.0100    0.0027
     7        1.2932             nan     0.0100    0.0026
     8        1.2880             nan     0.0100    0.0025
     9        1.2827             nan     0.0100    0.0025
    10        1.2773             nan     0.0100    0.0025
    20        1.2337             nan     0.0100    0.0020
    40        1.1660             nan     0.0100    0.0014
    60        1.1179             nan     0.0100    0.0011
    80        1.0803             nan     0.0100    0.0008
   100        1.0493             nan     0.0100    0.0007
   120        1.0231             nan     0.0100    0.0005
   140        1.0018             nan     0.0100    0.0004
   160        0.9836             nan     0.0100    0.0003
   180        0.9670             nan     0.0100    0.0002
   200        0.9528             nan     0.0100    0.0003
   220        0.9405             nan     0.0100    0.0002
   240        0.9290             nan     0.0100    0.0002
   260        0.9187             nan     0.0100    0.0001
   280        0.9100             nan     0.0100    0.0001
   300        0.9015             nan     0.0100    0.0002
   320        0.8940             nan     0.0100    0.0001
   340        0.8870             nan     0.0100    0.0001
   360        0.8810             nan     0.0100    0.0001
   380        0.8751             nan     0.0100    0.0000
   400        0.8700             nan     0.0100    0.0001
   420        0.8652             nan     0.0100   -0.0001
   440        0.8604             nan     0.0100    0.0001
   460        0.8564             nan     0.0100    0.0000
   480        0.8522             nan     0.0100    0.0000
   500        0.8484             nan     0.0100    0.0000
   520        0.8444             nan     0.0100    0.0000
   540        0.8409             nan     0.0100    0.0000
   560        0.8376             nan     0.0100    0.0000
   580        0.8343             nan     0.0100    0.0000
   600        0.8313             nan     0.0100    0.0000
   620        0.8284             nan     0.0100   -0.0000
   640        0.8258             nan     0.0100   -0.0000
   660        0.8231             nan     0.0100   -0.0000
   680        0.8205             nan     0.0100   -0.0000
   700        0.8177             nan     0.0100    0.0000
   720        0.8153             nan     0.0100   -0.0001
   740        0.8129             nan     0.0100   -0.0000
   760        0.8107             nan     0.0100   -0.0001
   780        0.8087             nan     0.0100   -0.0000
   800        0.8064             nan     0.0100   -0.0001
   820        0.8045             nan     0.0100    0.0000
   840        0.8026             nan     0.0100    0.0000
   860        0.8006             nan     0.0100   -0.0001
   880        0.7991             nan     0.0100   -0.0001
   900        0.7972             nan     0.0100    0.0000
   920        0.7952             nan     0.0100   -0.0000
   940        0.7936             nan     0.0100   -0.0000
   960        0.7916             nan     0.0100   -0.0000
   980        0.7902             nan     0.0100    0.0000
  1000        0.7886             nan     0.0100   -0.0000
  1020        0.7870             nan     0.0100    0.0000
  1040        0.7855             nan     0.0100   -0.0000
  1060        0.7841             nan     0.0100   -0.0000
  1080        0.7827             nan     0.0100   -0.0000
  1100        0.7814             nan     0.0100   -0.0001

- Fold09.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0037
     2        1.3164             nan     0.0100    0.0034
     3        1.3091             nan     0.0100    0.0035
     4        1.3017             nan     0.0100    0.0035
     5        1.2952             nan     0.0100    0.0035
     6        1.2886             nan     0.0100    0.0032
     7        1.2825             nan     0.0100    0.0033
     8        1.2763             nan     0.0100    0.0032
     9        1.2696             nan     0.0100    0.0031
    10        1.2635             nan     0.0100    0.0031
    20        1.2071             nan     0.0100    0.0027
    40        1.1168             nan     0.0100    0.0016
    60        1.0511             nan     0.0100    0.0014
    80        1.0015             nan     0.0100    0.0010
   100        0.9637             nan     0.0100    0.0008
   120        0.9335             nan     0.0100    0.0006
   140        0.9111             nan     0.0100    0.0004
   160        0.8925             nan     0.0100    0.0004
   180        0.8773             nan     0.0100    0.0001
   200        0.8640             nan     0.0100    0.0002
   220        0.8532             nan     0.0100    0.0000
   240        0.8429             nan     0.0100    0.0001
   260        0.8336             nan     0.0100    0.0001
   280        0.8256             nan     0.0100   -0.0001
   300        0.8178             nan     0.0100   -0.0000
   320        0.8105             nan     0.0100    0.0002
   340        0.8035             nan     0.0100   -0.0000
   360        0.7980             nan     0.0100   -0.0000
   380        0.7926             nan     0.0100    0.0001
   400        0.7875             nan     0.0100   -0.0000
   420        0.7824             nan     0.0100   -0.0000
   440        0.7779             nan     0.0100   -0.0001
   460        0.7728             nan     0.0100   -0.0001
   480        0.7683             nan     0.0100   -0.0002
   500        0.7645             nan     0.0100   -0.0001
   520        0.7608             nan     0.0100   -0.0001
   540        0.7574             nan     0.0100   -0.0002
   560        0.7539             nan     0.0100   -0.0000
   580        0.7507             nan     0.0100   -0.0001
   600        0.7477             nan     0.0100   -0.0000
   620        0.7445             nan     0.0100    0.0000
   640        0.7417             nan     0.0100   -0.0000
   660        0.7385             nan     0.0100   -0.0001
   680        0.7356             nan     0.0100   -0.0001
   700        0.7331             nan     0.0100   -0.0001
   720        0.7303             nan     0.0100   -0.0001
   740        0.7280             nan     0.0100   -0.0001
   760        0.7257             nan     0.0100   -0.0001
   780        0.7228             nan     0.0100   -0.0001
   800        0.7203             nan     0.0100   -0.0001
   820        0.7179             nan     0.0100   -0.0001
   840        0.7154             nan     0.0100   -0.0001
   860        0.7132             nan     0.0100   -0.0001
   880        0.7107             nan     0.0100    0.0000
   900        0.7089             nan     0.0100   -0.0001
   920        0.7065             nan     0.0100   -0.0001
   940        0.7043             nan     0.0100   -0.0001
   960        0.7021             nan     0.0100   -0.0000
   980        0.7004             nan     0.0100   -0.0001
  1000        0.6984             nan     0.0100   -0.0002
  1020        0.6965             nan     0.0100   -0.0000
  1040        0.6946             nan     0.0100    0.0000
  1060        0.6921             nan     0.0100   -0.0000
  1080        0.6904             nan     0.0100    0.0000
  1100        0.6885             nan     0.0100   -0.0001

- Fold09.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3235             nan     0.0100    0.0038
     2        1.3154             nan     0.0100    0.0039
     3        1.3076             nan     0.0100    0.0039
     4        1.2996             nan     0.0100    0.0041
     5        1.2922             nan     0.0100    0.0039
     6        1.2850             nan     0.0100    0.0036
     7        1.2776             nan     0.0100    0.0036
     8        1.2707             nan     0.0100    0.0032
     9        1.2639             nan     0.0100    0.0034
    10        1.2567             nan     0.0100    0.0033
    20        1.1932             nan     0.0100    0.0027
    40        1.0950             nan     0.0100    0.0018
    60        1.0225             nan     0.0100    0.0014
    80        0.9683             nan     0.0100    0.0006
   100        0.9283             nan     0.0100    0.0009
   120        0.8965             nan     0.0100    0.0005
   140        0.8721             nan     0.0100    0.0003
   160        0.8520             nan     0.0100    0.0003
   180        0.8356             nan     0.0100    0.0001
   200        0.8212             nan     0.0100    0.0001
   220        0.8099             nan     0.0100   -0.0000
   240        0.7983             nan     0.0100    0.0000
   260        0.7884             nan     0.0100    0.0000
   280        0.7803             nan     0.0100   -0.0000
   300        0.7727             nan     0.0100    0.0001
   320        0.7645             nan     0.0100   -0.0001
   340        0.7577             nan     0.0100    0.0001
   360        0.7511             nan     0.0100    0.0000
   380        0.7447             nan     0.0100    0.0001
   400        0.7390             nan     0.0100   -0.0000
   420        0.7344             nan     0.0100   -0.0000
   440        0.7296             nan     0.0100   -0.0000
   460        0.7249             nan     0.0100   -0.0002
   480        0.7200             nan     0.0100   -0.0001
   500        0.7155             nan     0.0100   -0.0001
   520        0.7113             nan     0.0100   -0.0000
   540        0.7073             nan     0.0100   -0.0000
   560        0.7030             nan     0.0100   -0.0001
   580        0.6991             nan     0.0100   -0.0001
   600        0.6950             nan     0.0100   -0.0000
   620        0.6907             nan     0.0100   -0.0000
   640        0.6874             nan     0.0100   -0.0001
   660        0.6838             nan     0.0100   -0.0001
   680        0.6808             nan     0.0100   -0.0000
   700        0.6777             nan     0.0100   -0.0002
   720        0.6745             nan     0.0100   -0.0002
   740        0.6712             nan     0.0100   -0.0003
   760        0.6684             nan     0.0100   -0.0002
   780        0.6653             nan     0.0100   -0.0001
   800        0.6620             nan     0.0100   -0.0001
   820        0.6590             nan     0.0100   -0.0001
   840        0.6560             nan     0.0100   -0.0001
   860        0.6531             nan     0.0100   -0.0002
   880        0.6504             nan     0.0100   -0.0001
   900        0.6477             nan     0.0100   -0.0000
   920        0.6456             nan     0.0100   -0.0001
   940        0.6430             nan     0.0100   -0.0001
   960        0.6404             nan     0.0100   -0.0001
   980        0.6378             nan     0.0100   -0.0001
  1000        0.6353             nan     0.0100   -0.0001
  1020        0.6329             nan     0.0100   -0.0001
  1040        0.6307             nan     0.0100   -0.0001
  1060        0.6282             nan     0.0100   -0.0001
  1080        0.6257             nan     0.0100   -0.0001
  1100        0.6224             nan     0.0100   -0.0000

- Fold09.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2726             nan     0.1000    0.0277
     2        1.2287             nan     0.1000    0.0237
     3        1.1932             nan     0.1000    0.0190
     4        1.1577             nan     0.1000    0.0148
     5        1.1365             nan     0.1000    0.0078
     6        1.1096             nan     0.1000    0.0116
     7        1.0892             nan     0.1000    0.0100
     8        1.0689             nan     0.1000    0.0075
     9        1.0513             nan     0.1000    0.0055
    10        1.0355             nan     0.1000    0.0061
    20        0.9466             nan     0.1000    0.0020
    40        0.8660             nan     0.1000    0.0003
    60        0.8307             nan     0.1000    0.0001
    80        0.8063             nan     0.1000   -0.0002
   100        0.7884             nan     0.1000   -0.0004
   120        0.7741             nan     0.1000   -0.0002
   140        0.7621             nan     0.1000   -0.0008
   160        0.7538             nan     0.1000   -0.0022
   180        0.7464             nan     0.1000    0.0000
   200        0.7401             nan     0.1000   -0.0005
   220        0.7352             nan     0.1000   -0.0008
   240        0.7286             nan     0.1000   -0.0007
   260        0.7240             nan     0.1000   -0.0009
   280        0.7194             nan     0.1000   -0.0003
   300        0.7172             nan     0.1000   -0.0004
   320        0.7119             nan     0.1000   -0.0008
   340        0.7068             nan     0.1000   -0.0006
   360        0.7045             nan     0.1000   -0.0009
   380        0.7014             nan     0.1000   -0.0012
   400        0.6971             nan     0.1000   -0.0015
   420        0.6950             nan     0.1000   -0.0006
   440        0.6919             nan     0.1000   -0.0008
   460        0.6877             nan     0.1000   -0.0007
   480        0.6858             nan     0.1000   -0.0011
   500        0.6835             nan     0.1000   -0.0004
   520        0.6805             nan     0.1000   -0.0013
   540        0.6783             nan     0.1000   -0.0010
   560        0.6757             nan     0.1000   -0.0013
   580        0.6732             nan     0.1000   -0.0013
   600        0.6700             nan     0.1000   -0.0006
   620        0.6689             nan     0.1000   -0.0001
   640        0.6661             nan     0.1000   -0.0010
   660        0.6643             nan     0.1000   -0.0015
   680        0.6626             nan     0.1000   -0.0005
   700        0.6605             nan     0.1000   -0.0013
   720        0.6597             nan     0.1000   -0.0014
   740        0.6565             nan     0.1000   -0.0006
   760        0.6544             nan     0.1000   -0.0003
   780        0.6535             nan     0.1000   -0.0002
   800        0.6515             nan     0.1000   -0.0009
   820        0.6494             nan     0.1000   -0.0010
   840        0.6483             nan     0.1000   -0.0019
   860        0.6459             nan     0.1000   -0.0005
   880        0.6447             nan     0.1000   -0.0002
   900        0.6426             nan     0.1000   -0.0014
   920        0.6416             nan     0.1000   -0.0008
   940        0.6404             nan     0.1000   -0.0006
   960        0.6392             nan     0.1000   -0.0007
   980        0.6375             nan     0.1000   -0.0004
  1000        0.6358             nan     0.1000   -0.0003
  1020        0.6337             nan     0.1000   -0.0005
  1040        0.6314             nan     0.1000   -0.0008
  1060        0.6306             nan     0.1000   -0.0004
  1080        0.6291             nan     0.1000   -0.0015
  1100        0.6286             nan     0.1000   -0.0008

- Fold09.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2573             nan     0.1000    0.0347
     2        1.1988             nan     0.1000    0.0294
     3        1.1544             nan     0.1000    0.0210
     4        1.1157             nan     0.1000    0.0204
     5        1.0803             nan     0.1000    0.0164
     6        1.0499             nan     0.1000    0.0151
     7        1.0214             nan     0.1000    0.0130
     8        0.9988             nan     0.1000    0.0101
     9        0.9782             nan     0.1000    0.0097
    10        0.9622             nan     0.1000    0.0076
    20        0.8576             nan     0.1000    0.0031
    40        0.7791             nan     0.1000    0.0005
    60        0.7419             nan     0.1000    0.0012
    80        0.7149             nan     0.1000   -0.0014
   100        0.6929             nan     0.1000   -0.0010
   120        0.6734             nan     0.1000   -0.0006
   140        0.6568             nan     0.1000   -0.0010
   160        0.6436             nan     0.1000   -0.0005
   180        0.6285             nan     0.1000   -0.0012
   200        0.6182             nan     0.1000   -0.0018
   220        0.6063             nan     0.1000   -0.0010
   240        0.5973             nan     0.1000   -0.0010
   260        0.5890             nan     0.1000   -0.0006
   280        0.5803             nan     0.1000   -0.0012
   300        0.5720             nan     0.1000   -0.0013
   320        0.5623             nan     0.1000   -0.0007
   340        0.5537             nan     0.1000   -0.0012
   360        0.5461             nan     0.1000   -0.0009
   380        0.5361             nan     0.1000   -0.0011
   400        0.5269             nan     0.1000   -0.0008
   420        0.5215             nan     0.1000   -0.0014
   440        0.5139             nan     0.1000   -0.0008
   460        0.5082             nan     0.1000   -0.0007
   480        0.5021             nan     0.1000   -0.0006
   500        0.4945             nan     0.1000   -0.0011
   520        0.4890             nan     0.1000   -0.0009
   540        0.4843             nan     0.1000   -0.0005
   560        0.4801             nan     0.1000   -0.0006
   580        0.4744             nan     0.1000   -0.0007
   600        0.4703             nan     0.1000   -0.0009
   620        0.4649             nan     0.1000   -0.0008
   640        0.4601             nan     0.1000   -0.0008
   660        0.4542             nan     0.1000   -0.0010
   680        0.4494             nan     0.1000   -0.0005
   700        0.4437             nan     0.1000   -0.0009
   720        0.4385             nan     0.1000   -0.0006
   740        0.4346             nan     0.1000   -0.0017
   760        0.4301             nan     0.1000   -0.0008
   780        0.4268             nan     0.1000   -0.0009
   800        0.4231             nan     0.1000   -0.0006
   820        0.4188             nan     0.1000   -0.0006
   840        0.4157             nan     0.1000   -0.0007
   860        0.4119             nan     0.1000   -0.0007
   880        0.4078             nan     0.1000   -0.0004
   900        0.4031             nan     0.1000   -0.0004
   920        0.3992             nan     0.1000   -0.0005
   940        0.3964             nan     0.1000   -0.0007
   960        0.3939             nan     0.1000   -0.0013
   980        0.3902             nan     0.1000   -0.0004
  1000        0.3864             nan     0.1000   -0.0008
  1020        0.3833             nan     0.1000   -0.0013
  1040        0.3818             nan     0.1000   -0.0008
  1060        0.3801             nan     0.1000   -0.0004
  1080        0.3764             nan     0.1000   -0.0014
  1100        0.3734             nan     0.1000   -0.0012

- Fold09.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
variable 37: CabinCodeT has no variation.
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2582             nan     0.1000    0.0377
     2        1.1871             nan     0.1000    0.0349
     3        1.1342             nan     0.1000    0.0263
     4        1.0865             nan     0.1000    0.0218
     5        1.0490             nan     0.1000    0.0170
     6        1.0162             nan     0.1000    0.0145
     7        0.9866             nan     0.1000    0.0133
     8        0.9627             nan     0.1000    0.0103
     9        0.9420             nan     0.1000    0.0094
    10        0.9277             nan     0.1000    0.0060
    20        0.8186             nan     0.1000    0.0037
    40        0.7412             nan     0.1000    0.0004
    60        0.6952             nan     0.1000   -0.0009
    80        0.6561             nan     0.1000   -0.0013
   100        0.6263             nan     0.1000   -0.0025
   120        0.6032             nan     0.1000   -0.0004
   140        0.5846             nan     0.1000   -0.0023
   160        0.5650             nan     0.1000   -0.0022
   180        0.5530             nan     0.1000   -0.0012
   200        0.5368             nan     0.1000   -0.0013
   220        0.5265             nan     0.1000   -0.0012
   240        0.5130             nan     0.1000   -0.0016
   260        0.5007             nan     0.1000   -0.0017
   280        0.4849             nan     0.1000   -0.0013
   300        0.4747             nan     0.1000   -0.0011
   320        0.4643             nan     0.1000   -0.0009
   340        0.4542             nan     0.1000   -0.0008
   360        0.4458             nan     0.1000   -0.0014
   380        0.4365             nan     0.1000   -0.0016
   400        0.4279             nan     0.1000   -0.0013
   420        0.4197             nan     0.1000   -0.0008
   440        0.4110             nan     0.1000   -0.0013
   460        0.4040             nan     0.1000   -0.0010
   480        0.3980             nan     0.1000   -0.0008
   500        0.3893             nan     0.1000   -0.0018
   520        0.3810             nan     0.1000   -0.0012
   540        0.3732             nan     0.1000   -0.0015
   560        0.3681             nan     0.1000   -0.0008
   580        0.3600             nan     0.1000   -0.0008
   600        0.3558             nan     0.1000   -0.0007
   620        0.3495             nan     0.1000   -0.0006
   640        0.3452             nan     0.1000   -0.0004
   660        0.3411             nan     0.1000   -0.0011
   680        0.3377             nan     0.1000   -0.0003
   700        0.3326             nan     0.1000   -0.0009
   720        0.3264             nan     0.1000   -0.0008
   740        0.3206             nan     0.1000   -0.0015
   760        0.3166             nan     0.1000   -0.0014
   780        0.3096             nan     0.1000   -0.0003
   800        0.3058             nan     0.1000   -0.0014
   820        0.2998             nan     0.1000   -0.0005
   840        0.2946             nan     0.1000   -0.0015
   860        0.2903             nan     0.1000   -0.0010
   880        0.2865             nan     0.1000   -0.0008
   900        0.2824             nan     0.1000   -0.0011
   920        0.2780             nan     0.1000   -0.0005
   940        0.2747             nan     0.1000   -0.0010
   960        0.2705             nan     0.1000   -0.0008
   980        0.2663             nan     0.1000   -0.0004
  1000        0.2622             nan     0.1000   -0.0007
  1020        0.2583             nan     0.1000   -0.0009
  1040        0.2544             nan     0.1000   -0.0009
  1060        0.2506             nan     0.1000   -0.0014
  1080        0.2465             nan     0.1000   -0.0010
  1100        0.2424             nan     0.1000   -0.0012

- Fold09.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3266             nan     0.0100    0.0027
     2        1.3210             nan     0.0100    0.0027
     3        1.3155             nan     0.0100    0.0026
     4        1.3107             nan     0.0100    0.0025
     5        1.3058             nan     0.0100    0.0025
     6        1.3001             nan     0.0100    0.0026
     7        1.2952             nan     0.0100    0.0025
     8        1.2907             nan     0.0100    0.0025
     9        1.2858             nan     0.0100    0.0024
    10        1.2813             nan     0.0100    0.0023
    20        1.2388             nan     0.0100    0.0019
    40        1.1742             nan     0.0100    0.0013
    60        1.1278             nan     0.0100    0.0009
    80        1.0931             nan     0.0100    0.0007
   100        1.0646             nan     0.0100    0.0006
   120        1.0408             nan     0.0100    0.0005
   140        1.0211             nan     0.0100    0.0003
   160        1.0036             nan     0.0100    0.0003
   180        0.9890             nan     0.0100    0.0003
   200        0.9767             nan     0.0100    0.0001
   220        0.9655             nan     0.0100    0.0002
   240        0.9559             nan     0.0100    0.0002
   260        0.9474             nan     0.0100    0.0001
   280        0.9393             nan     0.0100    0.0002
   300        0.9316             nan     0.0100   -0.0000
   320        0.9249             nan     0.0100    0.0001
   340        0.9192             nan     0.0100    0.0001
   360        0.9133             nan     0.0100   -0.0000
   380        0.9076             nan     0.0100    0.0001
   400        0.9026             nan     0.0100    0.0000
   420        0.8982             nan     0.0100   -0.0000
   440        0.8938             nan     0.0100    0.0000
   460        0.8898             nan     0.0100    0.0000
   480        0.8857             nan     0.0100   -0.0000
   500        0.8819             nan     0.0100    0.0001
   520        0.8784             nan     0.0100    0.0000
   540        0.8752             nan     0.0100   -0.0000
   560        0.8718             nan     0.0100   -0.0000
   580        0.8686             nan     0.0100    0.0000
   600        0.8657             nan     0.0100    0.0000
   620        0.8629             nan     0.0100    0.0000
   640        0.8602             nan     0.0100    0.0000
   660        0.8576             nan     0.0100    0.0001
   680        0.8551             nan     0.0100    0.0000
   700        0.8525             nan     0.0100   -0.0000
   720        0.8500             nan     0.0100   -0.0000
   740        0.8476             nan     0.0100   -0.0000
   760        0.8455             nan     0.0100   -0.0000
   780        0.8435             nan     0.0100   -0.0000
   800        0.8417             nan     0.0100   -0.0001
   820        0.8399             nan     0.0100   -0.0001
   840        0.8380             nan     0.0100   -0.0000
   860        0.8364             nan     0.0100   -0.0000
   880        0.8345             nan     0.0100   -0.0000
   900        0.8327             nan     0.0100   -0.0001
   920        0.8307             nan     0.0100   -0.0001
   940        0.8290             nan     0.0100   -0.0001
   960        0.8276             nan     0.0100   -0.0001
   980        0.8264             nan     0.0100   -0.0000
  1000        0.8249             nan     0.0100   -0.0001
  1020        0.8236             nan     0.0100   -0.0000
  1040        0.8222             nan     0.0100   -0.0000
  1060        0.8209             nan     0.0100   -0.0000
  1080        0.8194             nan     0.0100   -0.0001
  1100        0.8184             nan     0.0100   -0.0000

- Fold10.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3244             nan     0.0100    0.0035
     2        1.3169             nan     0.0100    0.0032
     3        1.3100             nan     0.0100    0.0031
     4        1.3032             nan     0.0100    0.0033
     5        1.2966             nan     0.0100    0.0032
     6        1.2903             nan     0.0100    0.0032
     7        1.2839             nan     0.0100    0.0032
     8        1.2782             nan     0.0100    0.0031
     9        1.2721             nan     0.0100    0.0029
    10        1.2664             nan     0.0100    0.0028
    20        1.2141             nan     0.0100    0.0023
    40        1.1307             nan     0.0100    0.0017
    60        1.0702             nan     0.0100    0.0012
    80        1.0249             nan     0.0100    0.0008
   100        0.9884             nan     0.0100    0.0007
   120        0.9617             nan     0.0100    0.0006
   140        0.9396             nan     0.0100    0.0004
   160        0.9223             nan     0.0100    0.0003
   180        0.9077             nan     0.0100    0.0002
   200        0.8963             nan     0.0100    0.0001
   220        0.8859             nan     0.0100   -0.0000
   240        0.8762             nan     0.0100    0.0001
   260        0.8669             nan     0.0100    0.0001
   280        0.8589             nan     0.0100   -0.0000
   300        0.8518             nan     0.0100    0.0000
   320        0.8454             nan     0.0100    0.0001
   340        0.8392             nan     0.0100   -0.0000
   360        0.8328             nan     0.0100    0.0000
   380        0.8271             nan     0.0100    0.0000
   400        0.8217             nan     0.0100   -0.0001
   420        0.8172             nan     0.0100   -0.0000
   440        0.8132             nan     0.0100    0.0000
   460        0.8094             nan     0.0100   -0.0000
   480        0.8054             nan     0.0100    0.0001
   500        0.8015             nan     0.0100   -0.0000
   520        0.7976             nan     0.0100   -0.0000
   540        0.7941             nan     0.0100   -0.0000
   560        0.7905             nan     0.0100    0.0001
   580        0.7876             nan     0.0100   -0.0001
   600        0.7844             nan     0.0100   -0.0001
   620        0.7813             nan     0.0100   -0.0001
   640        0.7780             nan     0.0100   -0.0000
   660        0.7750             nan     0.0100    0.0000
   680        0.7723             nan     0.0100   -0.0001
   700        0.7697             nan     0.0100   -0.0000
   720        0.7674             nan     0.0100    0.0000
   740        0.7648             nan     0.0100   -0.0000
   760        0.7621             nan     0.0100   -0.0000
   780        0.7591             nan     0.0100   -0.0001
   800        0.7572             nan     0.0100   -0.0001
   820        0.7549             nan     0.0100   -0.0000
   840        0.7524             nan     0.0100   -0.0001
   860        0.7500             nan     0.0100   -0.0001
   880        0.7477             nan     0.0100   -0.0000
   900        0.7453             nan     0.0100   -0.0001
   920        0.7428             nan     0.0100   -0.0001
   940        0.7411             nan     0.0100   -0.0002
   960        0.7391             nan     0.0100   -0.0002
   980        0.7371             nan     0.0100   -0.0001
  1000        0.7352             nan     0.0100   -0.0001
  1020        0.7330             nan     0.0100   -0.0001
  1040        0.7310             nan     0.0100   -0.0001
  1060        0.7288             nan     0.0100   -0.0001
  1080        0.7272             nan     0.0100   -0.0002
  1100        0.7254             nan     0.0100   -0.0001

- Fold10.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3243             nan     0.0100    0.0039
     2        1.3164             nan     0.0100    0.0039
     3        1.3093             nan     0.0100    0.0036
     4        1.3018             nan     0.0100    0.0033
     5        1.2943             nan     0.0100    0.0035
     6        1.2874             nan     0.0100    0.0034
     7        1.2799             nan     0.0100    0.0030
     8        1.2729             nan     0.0100    0.0033
     9        1.2663             nan     0.0100    0.0034
    10        1.2599             nan     0.0100    0.0033
    20        1.2014             nan     0.0100    0.0024
    40        1.1093             nan     0.0100    0.0018
    60        1.0414             nan     0.0100    0.0013
    80        0.9924             nan     0.0100    0.0010
   100        0.9527             nan     0.0100    0.0008
   120        0.9240             nan     0.0100    0.0006
   140        0.9017             nan     0.0100    0.0004
   160        0.8831             nan     0.0100    0.0003
   180        0.8676             nan     0.0100    0.0002
   200        0.8538             nan     0.0100    0.0001
   220        0.8422             nan     0.0100    0.0002
   240        0.8320             nan     0.0100    0.0000
   260        0.8229             nan     0.0100    0.0001
   280        0.8148             nan     0.0100   -0.0001
   300        0.8077             nan     0.0100   -0.0001
   320        0.8000             nan     0.0100    0.0000
   340        0.7939             nan     0.0100    0.0001
   360        0.7879             nan     0.0100   -0.0001
   380        0.7825             nan     0.0100   -0.0001
   400        0.7773             nan     0.0100   -0.0000
   420        0.7724             nan     0.0100   -0.0001
   440        0.7671             nan     0.0100   -0.0000
   460        0.7625             nan     0.0100   -0.0001
   480        0.7577             nan     0.0100   -0.0000
   500        0.7530             nan     0.0100   -0.0000
   520        0.7489             nan     0.0100   -0.0000
   540        0.7451             nan     0.0100   -0.0001
   560        0.7414             nan     0.0100   -0.0001
   580        0.7377             nan     0.0100   -0.0000
   600        0.7344             nan     0.0100   -0.0002
   620        0.7311             nan     0.0100   -0.0002
   640        0.7275             nan     0.0100    0.0000
   660        0.7246             nan     0.0100   -0.0002
   680        0.7212             nan     0.0100   -0.0001
   700        0.7179             nan     0.0100   -0.0000
   720        0.7148             nan     0.0100   -0.0001
   740        0.7116             nan     0.0100   -0.0001
   760        0.7086             nan     0.0100   -0.0001
   780        0.7063             nan     0.0100   -0.0001
   800        0.7035             nan     0.0100   -0.0002
   820        0.7002             nan     0.0100   -0.0002
   840        0.6977             nan     0.0100   -0.0002
   860        0.6948             nan     0.0100   -0.0001
   880        0.6914             nan     0.0100   -0.0001
   900        0.6882             nan     0.0100   -0.0001
   920        0.6857             nan     0.0100   -0.0002
   940        0.6832             nan     0.0100   -0.0000
   960        0.6805             nan     0.0100   -0.0000
   980        0.6780             nan     0.0100   -0.0001
  1000        0.6750             nan     0.0100   -0.0001
  1020        0.6723             nan     0.0100   -0.0000
  1040        0.6700             nan     0.0100   -0.0001
  1060        0.6673             nan     0.0100   -0.0001
  1080        0.6651             nan     0.0100   -0.0002
  1100        0.6628             nan     0.0100   -0.0001

- Fold10.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2811             nan     0.1000    0.0263
     2        1.2358             nan     0.1000    0.0208
     3        1.2003             nan     0.1000    0.0167
     4        1.1715             nan     0.1000    0.0136
     5        1.1459             nan     0.1000    0.0130
     6        1.1271             nan     0.1000    0.0082
     7        1.1069             nan     0.1000    0.0082
     8        1.0900             nan     0.1000    0.0083
     9        1.0747             nan     0.1000    0.0060
    10        1.0600             nan     0.1000    0.0064
    20        0.9761             nan     0.1000    0.0020
    40        0.9034             nan     0.1000   -0.0002
    60        0.8692             nan     0.1000   -0.0005
    80        0.8464             nan     0.1000   -0.0013
   100        0.8278             nan     0.1000   -0.0006
   120        0.8148             nan     0.1000   -0.0003
   140        0.8047             nan     0.1000   -0.0004
   160        0.7953             nan     0.1000   -0.0009
   180        0.7897             nan     0.1000   -0.0003
   200        0.7817             nan     0.1000   -0.0003
   220        0.7744             nan     0.1000   -0.0009
   240        0.7697             nan     0.1000   -0.0006
   260        0.7660             nan     0.1000   -0.0013
   280        0.7605             nan     0.1000   -0.0004
   300        0.7568             nan     0.1000   -0.0006
   320        0.7541             nan     0.1000   -0.0011
   340        0.7505             nan     0.1000   -0.0005
   360        0.7468             nan     0.1000   -0.0008
   380        0.7430             nan     0.1000   -0.0001
   400        0.7403             nan     0.1000   -0.0006
   420        0.7357             nan     0.1000   -0.0006
   440        0.7326             nan     0.1000   -0.0012
   460        0.7306             nan     0.1000   -0.0004
   480        0.7281             nan     0.1000   -0.0013
   500        0.7261             nan     0.1000   -0.0003
   520        0.7231             nan     0.1000   -0.0005
   540        0.7212             nan     0.1000   -0.0005
   560        0.7192             nan     0.1000   -0.0010
   580        0.7180             nan     0.1000   -0.0003
   600        0.7154             nan     0.1000   -0.0006
   620        0.7141             nan     0.1000   -0.0011
   640        0.7116             nan     0.1000   -0.0005
   660        0.7099             nan     0.1000   -0.0006
   680        0.7080             nan     0.1000   -0.0011
   700        0.7080             nan     0.1000   -0.0005
   720        0.7054             nan     0.1000   -0.0014
   740        0.7033             nan     0.1000   -0.0007
   760        0.7012             nan     0.1000   -0.0005
   780        0.6988             nan     0.1000   -0.0006
   800        0.6969             nan     0.1000   -0.0007
   820        0.6961             nan     0.1000   -0.0006
   840        0.6945             nan     0.1000   -0.0005
   860        0.6925             nan     0.1000   -0.0011
   880        0.6904             nan     0.1000   -0.0012
   900        0.6892             nan     0.1000   -0.0008
   920        0.6874             nan     0.1000   -0.0013
   940        0.6858             nan     0.1000   -0.0011
   960        0.6837             nan     0.1000   -0.0006
   980        0.6826             nan     0.1000   -0.0011
  1000        0.6822             nan     0.1000   -0.0004
  1020        0.6809             nan     0.1000   -0.0005
  1040        0.6799             nan     0.1000   -0.0010
  1060        0.6785             nan     0.1000   -0.0007
  1080        0.6766             nan     0.1000   -0.0003
  1100        0.6751             nan     0.1000   -0.0019

- Fold10.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2664             nan     0.1000    0.0321
     2        1.2099             nan     0.1000    0.0256
     3        1.1637             nan     0.1000    0.0211
     4        1.1230             nan     0.1000    0.0198
     5        1.0900             nan     0.1000    0.0157
     6        1.0608             nan     0.1000    0.0119
     7        1.0382             nan     0.1000    0.0110
     8        1.0174             nan     0.1000    0.0096
     9        0.9990             nan     0.1000    0.0088
    10        0.9820             nan     0.1000    0.0071
    20        0.8933             nan     0.1000   -0.0009
    40        0.8250             nan     0.1000    0.0001
    60        0.7875             nan     0.1000   -0.0006
    80        0.7642             nan     0.1000   -0.0014
   100        0.7411             nan     0.1000   -0.0009
   120        0.7274             nan     0.1000   -0.0008
   140        0.7110             nan     0.1000   -0.0012
   160        0.7003             nan     0.1000   -0.0014
   180        0.6818             nan     0.1000   -0.0012
   200        0.6682             nan     0.1000    0.0003
   220        0.6575             nan     0.1000   -0.0017
   240        0.6457             nan     0.1000   -0.0009
   260        0.6326             nan     0.1000   -0.0010
   280        0.6202             nan     0.1000   -0.0015
   300        0.6097             nan     0.1000   -0.0007
   320        0.6024             nan     0.1000   -0.0010
   340        0.5946             nan     0.1000   -0.0009
   360        0.5889             nan     0.1000   -0.0008
   380        0.5809             nan     0.1000   -0.0011
   400        0.5732             nan     0.1000   -0.0009
   420        0.5656             nan     0.1000   -0.0017
   440        0.5589             nan     0.1000   -0.0005
   460        0.5530             nan     0.1000   -0.0007
   480        0.5461             nan     0.1000   -0.0008
   500        0.5404             nan     0.1000   -0.0008
   520        0.5343             nan     0.1000   -0.0007
   540        0.5284             nan     0.1000   -0.0009
   560        0.5236             nan     0.1000   -0.0010
   580        0.5204             nan     0.1000   -0.0017
   600        0.5158             nan     0.1000   -0.0008
   620        0.5116             nan     0.1000   -0.0011
   640        0.5062             nan     0.1000   -0.0008
   660        0.5019             nan     0.1000   -0.0002
   680        0.4978             nan     0.1000   -0.0005
   700        0.4930             nan     0.1000   -0.0009
   720        0.4883             nan     0.1000   -0.0017
   740        0.4839             nan     0.1000   -0.0009
   760        0.4785             nan     0.1000   -0.0008
   780        0.4742             nan     0.1000   -0.0010
   800        0.4691             nan     0.1000   -0.0005
   820        0.4637             nan     0.1000   -0.0005
   840        0.4601             nan     0.1000   -0.0002
   860        0.4561             nan     0.1000   -0.0015
   880        0.4521             nan     0.1000   -0.0009
   900        0.4482             nan     0.1000   -0.0006
   920        0.4444             nan     0.1000   -0.0009
   940        0.4414             nan     0.1000   -0.0007
   960        0.4367             nan     0.1000   -0.0008
   980        0.4344             nan     0.1000   -0.0014
  1000        0.4324             nan     0.1000   -0.0011
  1020        0.4293             nan     0.1000   -0.0007
  1040        0.4257             nan     0.1000   -0.0005
  1060        0.4225             nan     0.1000   -0.0003
  1080        0.4188             nan     0.1000   -0.0008
  1100        0.4149             nan     0.1000   -0.0010

- Fold10.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100 
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.2549             nan     0.1000    0.0340
     2        1.1983             nan     0.1000    0.0267
     3        1.1467             nan     0.1000    0.0215
     4        1.1076             nan     0.1000    0.0183
     5        1.0669             nan     0.1000    0.0165
     6        1.0356             nan     0.1000    0.0139
     7        1.0078             nan     0.1000    0.0133
     8        0.9895             nan     0.1000    0.0069
     9        0.9700             nan     0.1000    0.0086
    10        0.9533             nan     0.1000    0.0089
    20        0.8542             nan     0.1000    0.0018
    40        0.7820             nan     0.1000   -0.0014
    60        0.7416             nan     0.1000   -0.0006
    80        0.7069             nan     0.1000   -0.0011
   100        0.6797             nan     0.1000   -0.0010
   120        0.6615             nan     0.1000   -0.0015
   140        0.6405             nan     0.1000   -0.0008
   160        0.6249             nan     0.1000   -0.0017
   180        0.6052             nan     0.1000   -0.0011
   200        0.5907             nan     0.1000   -0.0013
   220        0.5740             nan     0.1000   -0.0006
   240        0.5568             nan     0.1000   -0.0004
   260        0.5409             nan     0.1000   -0.0003
   280        0.5299             nan     0.1000   -0.0012
   300        0.5188             nan     0.1000   -0.0012
   320        0.5065             nan     0.1000   -0.0011
   340        0.4955             nan     0.1000   -0.0005
   360        0.4852             nan     0.1000   -0.0005
   380        0.4747             nan     0.1000   -0.0010
   400        0.4663             nan     0.1000   -0.0009
   420        0.4569             nan     0.1000   -0.0010
   440        0.4489             nan     0.1000   -0.0007
   460        0.4399             nan     0.1000   -0.0008
   480        0.4323             nan     0.1000   -0.0007
   500        0.4245             nan     0.1000   -0.0010
   520        0.4194             nan     0.1000   -0.0013
   540        0.4141             nan     0.1000   -0.0015
   560        0.4085             nan     0.1000   -0.0015
   580        0.4029             nan     0.1000   -0.0012
   600        0.3963             nan     0.1000   -0.0013
   620        0.3907             nan     0.1000   -0.0011
   640        0.3868             nan     0.1000   -0.0021
   660        0.3808             nan     0.1000   -0.0009
   680        0.3755             nan     0.1000   -0.0003
   700        0.3709             nan     0.1000   -0.0012
   720        0.3648             nan     0.1000   -0.0004
   740        0.3599             nan     0.1000   -0.0012
   760        0.3538             nan     0.1000   -0.0009
   780        0.3497             nan     0.1000   -0.0014
   800        0.3439             nan     0.1000   -0.0012
   820        0.3394             nan     0.1000   -0.0007
   840        0.3358             nan     0.1000   -0.0009
   860        0.3316             nan     0.1000   -0.0005
   880        0.3282             nan     0.1000   -0.0009
   900        0.3241             nan     0.1000   -0.0007
   920        0.3207             nan     0.1000   -0.0013
   940        0.3182             nan     0.1000   -0.0007
   960        0.3142             nan     0.1000   -0.0012
   980        0.3093             nan     0.1000   -0.0008
  1000        0.3058             nan     0.1000   -0.0008
  1020        0.3016             nan     0.1000   -0.0010
  1040        0.2977             nan     0.1000   -0.0006
  1060        0.2940             nan     0.1000   -0.0009
  1080        0.2892             nan     0.1000   -0.0010
  1100        0.2855             nan     0.1000   -0.0007

- Fold10.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100 
Aggregating results
Selecting tuning parameters
Fitting n.trees = 1100, interaction.depth = 3, shrinkage = 0.01, n.minobsinnode = 10 on full training set
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        1.3241             nan     0.0100    0.0040
     2        1.3155             nan     0.0100    0.0039
     3        1.3076             nan     0.0100    0.0041
     4        1.3003             nan     0.0100    0.0034
     5        1.2924             nan     0.0100    0.0038
     6        1.2854             nan     0.0100    0.0034
     7        1.2780             nan     0.0100    0.0032
     8        1.2707             nan     0.0100    0.0035
     9        1.2634             nan     0.0100    0.0034
    10        1.2569             nan     0.0100    0.0033
    20        1.1942             nan     0.0100    0.0029
    40        1.0986             nan     0.0100    0.0019
    60        1.0287             nan     0.0100    0.0015
    80        0.9768             nan     0.0100    0.0010
   100        0.9373             nan     0.0100    0.0007
   120        0.9069             nan     0.0100    0.0002
   140        0.8838             nan     0.0100    0.0004
   160        0.8639             nan     0.0100    0.0004
   180        0.8472             nan     0.0100    0.0001
   200        0.8327             nan     0.0100    0.0002
   220        0.8204             nan     0.0100    0.0001
   240        0.8095             nan     0.0100    0.0001
   260        0.7999             nan     0.0100    0.0001
   280        0.7912             nan     0.0100    0.0001
   300        0.7826             nan     0.0100    0.0001
   320        0.7756             nan     0.0100   -0.0000
   340        0.7691             nan     0.0100    0.0000
   360        0.7629             nan     0.0100    0.0000
   380        0.7569             nan     0.0100    0.0000
   400        0.7518             nan     0.0100   -0.0001
   420        0.7466             nan     0.0100   -0.0001
   440        0.7410             nan     0.0100   -0.0001
   460        0.7365             nan     0.0100    0.0000
   480        0.7322             nan     0.0100    0.0000
   500        0.7280             nan     0.0100   -0.0001
   520        0.7235             nan     0.0100   -0.0000
   540        0.7197             nan     0.0100    0.0000
   560        0.7158             nan     0.0100   -0.0000
   580        0.7121             nan     0.0100   -0.0001
   600        0.7079             nan     0.0100   -0.0000
   620        0.7043             nan     0.0100   -0.0001
   640        0.7006             nan     0.0100   -0.0001
   660        0.6973             nan     0.0100   -0.0001
   680        0.6939             nan     0.0100   -0.0001
   700        0.6910             nan     0.0100   -0.0001
   720        0.6883             nan     0.0100   -0.0000
   740        0.6850             nan     0.0100   -0.0000
   760        0.6826             nan     0.0100   -0.0000
   780        0.6799             nan     0.0100   -0.0001
   800        0.6773             nan     0.0100   -0.0001
   820        0.6743             nan     0.0100   -0.0000
   840        0.6715             nan     0.0100   -0.0001
   860        0.6689             nan     0.0100   -0.0001
   880        0.6660             nan     0.0100   -0.0002
   900        0.6631             nan     0.0100   -0.0001
   920        0.6606             nan     0.0100   -0.0001
   940        0.6585             nan     0.0100   -0.0001
   960        0.6560             nan     0.0100   -0.0001
   980        0.6532             nan     0.0100   -0.0001
  1000        0.6506             nan     0.0100   -0.0001
  1020        0.6483             nan     0.0100   -0.0002
  1040        0.6456             nan     0.0100   -0.0002
  1060        0.6433             nan     0.0100   -0.0001
  1080        0.6407             nan     0.0100   -0.0001
  1100        0.6391             nan     0.0100   -0.0001
boostFit
Stochastic Gradient Boosting 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Resampling results across tuning parameters:

  shrinkage  interaction.depth  n.trees  ROC        Sens       Spec     
  0.01       1                   500     0.8687740  0.7176975  0.8812727
  0.01       1                   700     0.8715465  0.7310756  0.8827340
  0.01       1                   900     0.8731344  0.7456975  0.8798182
  0.01       1                  1100     0.8737417  0.7614454  0.8790909
  0.01       2                   500     0.8768379  0.7410252  0.8819933
  0.01       2                   700     0.8781806  0.7549916  0.8808956
  0.01       2                   900     0.8791223  0.7614118  0.8801751
  0.01       2                  1100     0.8801043  0.7613950  0.8809024
  0.01       3                   500     0.8790775  0.7526218  0.8823636
  0.01       3                   700     0.8808075  0.7584874  0.8812727
  0.01       3                   900     0.8815746  0.7549748  0.8809091
  0.01       3                  1100     0.8817104  0.7532269  0.8849091
  0.10       1                   500     0.8699484  0.7480168  0.8721751
  0.10       1                   700     0.8708636  0.7485882  0.8736162
  0.10       1                   900     0.8686151  0.7422017  0.8743367
  0.10       1                  1100     0.8689881  0.7428067  0.8725051
  0.10       2                   500     0.8801393  0.7473950  0.8837845
  0.10       2                   700     0.8799571  0.7467563  0.8819798
  0.10       2                   900     0.8790711  0.7438992  0.8790370
  0.10       2                  1100     0.8773468  0.7474118  0.8757778
  0.10       3                   500     0.8803987  0.7450252  0.8790774
  0.10       3                   700     0.8805280  0.7438655  0.8746869
  0.10       3                   900     0.8793306  0.7438655  0.8692189
  0.10       3                  1100     0.8785908  0.7432773  0.8713872

Tuning parameter 'n.minobsinnode' was held constant at a value of 10
ROC was used to select the optimal model using  the largest value.
The final values used for the model were n.trees = 1100, interaction.depth = 3, shrinkage
 = 0.01 and n.minobsinnode = 10.
plot(boostFit)

xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')

plot(varImp(boostFit))

Random Forest (rf)

adapt_ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     adaptive = list(min = 5, alpha = 0.05, 
                                             method = "gls", complete = TRUE),
                     search = 'random'
                     )
rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfFit.y <- train(
    form = Survived~.,
    data = train.imp,
    method = 'rf',
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = rfGrid,
    verbose = TRUE,
    ntree = 400
)
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: mtry= 5 
- Fold01.Rep1: mtry= 5 
+ Fold01.Rep1: mtry=10 
- Fold01.Rep1: mtry=10 
+ Fold01.Rep1: mtry=15 
- Fold01.Rep1: mtry=15 
+ Fold01.Rep1: mtry=20 
- Fold01.Rep1: mtry=20 
+ Fold01.Rep1: mtry=25 
- Fold01.Rep1: mtry=25 
+ Fold02.Rep1: mtry= 5 
- Fold02.Rep1: mtry= 5 
+ Fold02.Rep1: mtry=10 
- Fold02.Rep1: mtry=10 
+ Fold02.Rep1: mtry=15 
- Fold02.Rep1: mtry=15 
+ Fold02.Rep1: mtry=20 
- Fold02.Rep1: mtry=20 
+ Fold02.Rep1: mtry=25 
- Fold02.Rep1: mtry=25 
+ Fold03.Rep1: mtry= 5 
- Fold03.Rep1: mtry= 5 
+ Fold03.Rep1: mtry=10 
- Fold03.Rep1: mtry=10 
+ Fold03.Rep1: mtry=15 
- Fold03.Rep1: mtry=15 
+ Fold03.Rep1: mtry=20 
- Fold03.Rep1: mtry=20 
+ Fold03.Rep1: mtry=25 
- Fold03.Rep1: mtry=25 
+ Fold04.Rep1: mtry= 5 
- Fold04.Rep1: mtry= 5 
+ Fold04.Rep1: mtry=10 
- Fold04.Rep1: mtry=10 
+ Fold04.Rep1: mtry=15 
- Fold04.Rep1: mtry=15 
+ Fold04.Rep1: mtry=20 
- Fold04.Rep1: mtry=20 
+ Fold04.Rep1: mtry=25 
- Fold04.Rep1: mtry=25 
+ Fold05.Rep1: mtry= 5 
- Fold05.Rep1: mtry= 5 
+ Fold05.Rep1: mtry=10 
- Fold05.Rep1: mtry=10 
+ Fold05.Rep1: mtry=15 
- Fold05.Rep1: mtry=15 
+ Fold05.Rep1: mtry=20 
- Fold05.Rep1: mtry=20 
+ Fold05.Rep1: mtry=25 
- Fold05.Rep1: mtry=25 
+ Fold06.Rep1: mtry= 5 
- Fold06.Rep1: mtry= 5 
+ Fold06.Rep1: mtry=10 
- Fold06.Rep1: mtry=10 
+ Fold06.Rep1: mtry=15 
- Fold06.Rep1: mtry=15 
+ Fold06.Rep1: mtry=20 
- Fold06.Rep1: mtry=20 
+ Fold06.Rep1: mtry=25 
- Fold06.Rep1: mtry=25 
+ Fold07.Rep1: mtry= 5 
- Fold07.Rep1: mtry= 5 
+ Fold07.Rep1: mtry=10 
- Fold07.Rep1: mtry=10 
+ Fold07.Rep1: mtry=15 
- Fold07.Rep1: mtry=15 
+ Fold07.Rep1: mtry=20 
- Fold07.Rep1: mtry=20 
+ Fold07.Rep1: mtry=25 
- Fold07.Rep1: mtry=25 
+ Fold08.Rep1: mtry= 5 
- Fold08.Rep1: mtry= 5 
+ Fold08.Rep1: mtry=10 
- Fold08.Rep1: mtry=10 
+ Fold08.Rep1: mtry=15 
- Fold08.Rep1: mtry=15 
+ Fold08.Rep1: mtry=20 
- Fold08.Rep1: mtry=20 
+ Fold08.Rep1: mtry=25 
- Fold08.Rep1: mtry=25 
+ Fold09.Rep1: mtry= 5 
- Fold09.Rep1: mtry= 5 
+ Fold09.Rep1: mtry=10 
- Fold09.Rep1: mtry=10 
+ Fold09.Rep1: mtry=15 
- Fold09.Rep1: mtry=15 
+ Fold09.Rep1: mtry=20 
- Fold09.Rep1: mtry=20 
+ Fold09.Rep1: mtry=25 
- Fold09.Rep1: mtry=25 
+ Fold10.Rep1: mtry= 5 
- Fold10.Rep1: mtry= 5 
+ Fold10.Rep1: mtry=10 
- Fold10.Rep1: mtry=10 
+ Fold10.Rep1: mtry=15 
- Fold10.Rep1: mtry=15 
+ Fold10.Rep1: mtry=20 
- Fold10.Rep1: mtry=20 
+ Fold10.Rep1: mtry=25 
- Fold10.Rep1: mtry=25 
+ Fold01.Rep2: mtry= 5 
- Fold01.Rep2: mtry= 5 
+ Fold01.Rep2: mtry=10 
- Fold01.Rep2: mtry=10 
+ Fold01.Rep2: mtry=15 
- Fold01.Rep2: mtry=15 
+ Fold01.Rep2: mtry=20 
- Fold01.Rep2: mtry=20 
+ Fold01.Rep2: mtry=25 
- Fold01.Rep2: mtry=25 
+ Fold02.Rep2: mtry= 5 
- Fold02.Rep2: mtry= 5 
+ Fold02.Rep2: mtry=10 
- Fold02.Rep2: mtry=10 
+ Fold02.Rep2: mtry=15 
- Fold02.Rep2: mtry=15 
+ Fold02.Rep2: mtry=20 
- Fold02.Rep2: mtry=20 
+ Fold02.Rep2: mtry=25 
- Fold02.Rep2: mtry=25 
+ Fold03.Rep2: mtry= 5 
- Fold03.Rep2: mtry= 5 
+ Fold03.Rep2: mtry=10 
- Fold03.Rep2: mtry=10 
+ Fold03.Rep2: mtry=15 
- Fold03.Rep2: mtry=15 
+ Fold03.Rep2: mtry=20 
- Fold03.Rep2: mtry=20 
+ Fold03.Rep2: mtry=25 
- Fold03.Rep2: mtry=25 
+ Fold04.Rep2: mtry= 5 
- Fold04.Rep2: mtry= 5 
+ Fold04.Rep2: mtry=10 
- Fold04.Rep2: mtry=10 
+ Fold04.Rep2: mtry=15 
- Fold04.Rep2: mtry=15 
+ Fold04.Rep2: mtry=20 
- Fold04.Rep2: mtry=20 
+ Fold04.Rep2: mtry=25 
- Fold04.Rep2: mtry=25 
+ Fold05.Rep2: mtry= 5 
- Fold05.Rep2: mtry= 5 
+ Fold05.Rep2: mtry=10 
- Fold05.Rep2: mtry=10 
+ Fold05.Rep2: mtry=15 
- Fold05.Rep2: mtry=15 
+ Fold05.Rep2: mtry=20 
- Fold05.Rep2: mtry=20 
+ Fold05.Rep2: mtry=25 
- Fold05.Rep2: mtry=25 
+ Fold06.Rep2: mtry= 5 
- Fold06.Rep2: mtry= 5 
+ Fold06.Rep2: mtry=10 
- Fold06.Rep2: mtry=10 
+ Fold06.Rep2: mtry=15 
- Fold06.Rep2: mtry=15 
+ Fold06.Rep2: mtry=20 
- Fold06.Rep2: mtry=20 
+ Fold06.Rep2: mtry=25 
- Fold06.Rep2: mtry=25 
+ Fold07.Rep2: mtry= 5 
- Fold07.Rep2: mtry= 5 
+ Fold07.Rep2: mtry=10 
- Fold07.Rep2: mtry=10 
+ Fold07.Rep2: mtry=15 
- Fold07.Rep2: mtry=15 
+ Fold07.Rep2: mtry=20 
- Fold07.Rep2: mtry=20 
+ Fold07.Rep2: mtry=25 
- Fold07.Rep2: mtry=25 
+ Fold08.Rep2: mtry= 5 
- Fold08.Rep2: mtry= 5 
+ Fold08.Rep2: mtry=10 
- Fold08.Rep2: mtry=10 
+ Fold08.Rep2: mtry=15 
- Fold08.Rep2: mtry=15 
+ Fold08.Rep2: mtry=20 
- Fold08.Rep2: mtry=20 
+ Fold08.Rep2: mtry=25 
- Fold08.Rep2: mtry=25 
+ Fold09.Rep2: mtry= 5 
- Fold09.Rep2: mtry= 5 
+ Fold09.Rep2: mtry=10 
- Fold09.Rep2: mtry=10 
+ Fold09.Rep2: mtry=15 
- Fold09.Rep2: mtry=15 
+ Fold09.Rep2: mtry=20 
- Fold09.Rep2: mtry=20 
+ Fold09.Rep2: mtry=25 
- Fold09.Rep2: mtry=25 
+ Fold10.Rep2: mtry= 5 
- Fold10.Rep2: mtry= 5 
+ Fold10.Rep2: mtry=10 
- Fold10.Rep2: mtry=10 
+ Fold10.Rep2: mtry=15 
- Fold10.Rep2: mtry=15 
+ Fold10.Rep2: mtry=20 
- Fold10.Rep2: mtry=20 
+ Fold10.Rep2: mtry=25 
- Fold10.Rep2: mtry=25 
+ Fold01.Rep3: mtry= 5 
- Fold01.Rep3: mtry= 5 
+ Fold01.Rep3: mtry=10 
- Fold01.Rep3: mtry=10 
+ Fold01.Rep3: mtry=15 
- Fold01.Rep3: mtry=15 
+ Fold01.Rep3: mtry=20 
- Fold01.Rep3: mtry=20 
+ Fold01.Rep3: mtry=25 
- Fold01.Rep3: mtry=25 
+ Fold02.Rep3: mtry= 5 
- Fold02.Rep3: mtry= 5 
+ Fold02.Rep3: mtry=10 
- Fold02.Rep3: mtry=10 
+ Fold02.Rep3: mtry=15 
- Fold02.Rep3: mtry=15 
+ Fold02.Rep3: mtry=20 
- Fold02.Rep3: mtry=20 
+ Fold02.Rep3: mtry=25 
- Fold02.Rep3: mtry=25 
+ Fold03.Rep3: mtry= 5 
- Fold03.Rep3: mtry= 5 
+ Fold03.Rep3: mtry=10 
- Fold03.Rep3: mtry=10 
+ Fold03.Rep3: mtry=15 
- Fold03.Rep3: mtry=15 
+ Fold03.Rep3: mtry=20 
- Fold03.Rep3: mtry=20 
+ Fold03.Rep3: mtry=25 
- Fold03.Rep3: mtry=25 
+ Fold04.Rep3: mtry= 5 
- Fold04.Rep3: mtry= 5 
+ Fold04.Rep3: mtry=10 
- Fold04.Rep3: mtry=10 
+ Fold04.Rep3: mtry=15 
- Fold04.Rep3: mtry=15 
+ Fold04.Rep3: mtry=20 
- Fold04.Rep3: mtry=20 
+ Fold04.Rep3: mtry=25 
- Fold04.Rep3: mtry=25 
+ Fold05.Rep3: mtry= 5 
- Fold05.Rep3: mtry= 5 
+ Fold05.Rep3: mtry=10 
- Fold05.Rep3: mtry=10 
+ Fold05.Rep3: mtry=15 
- Fold05.Rep3: mtry=15 
+ Fold05.Rep3: mtry=20 
- Fold05.Rep3: mtry=20 
+ Fold05.Rep3: mtry=25 
- Fold05.Rep3: mtry=25 
+ Fold06.Rep3: mtry= 5 
- Fold06.Rep3: mtry= 5 
+ Fold06.Rep3: mtry=10 
- Fold06.Rep3: mtry=10 
+ Fold06.Rep3: mtry=15 
- Fold06.Rep3: mtry=15 
+ Fold06.Rep3: mtry=20 
- Fold06.Rep3: mtry=20 
+ Fold06.Rep3: mtry=25 
- Fold06.Rep3: mtry=25 
+ Fold07.Rep3: mtry= 5 
- Fold07.Rep3: mtry= 5 
+ Fold07.Rep3: mtry=10 
- Fold07.Rep3: mtry=10 
+ Fold07.Rep3: mtry=15 
- Fold07.Rep3: mtry=15 
+ Fold07.Rep3: mtry=20 
- Fold07.Rep3: mtry=20 
+ Fold07.Rep3: mtry=25 
- Fold07.Rep3: mtry=25 
+ Fold08.Rep3: mtry= 5 
- Fold08.Rep3: mtry= 5 
+ Fold08.Rep3: mtry=10 
- Fold08.Rep3: mtry=10 
+ Fold08.Rep3: mtry=15 
- Fold08.Rep3: mtry=15 
+ Fold08.Rep3: mtry=20 
- Fold08.Rep3: mtry=20 
+ Fold08.Rep3: mtry=25 
- Fold08.Rep3: mtry=25 
+ Fold09.Rep3: mtry= 5 
- Fold09.Rep3: mtry= 5 
+ Fold09.Rep3: mtry=10 
- Fold09.Rep3: mtry=10 
+ Fold09.Rep3: mtry=15 
- Fold09.Rep3: mtry=15 
+ Fold09.Rep3: mtry=20 
- Fold09.Rep3: mtry=20 
+ Fold09.Rep3: mtry=25 
- Fold09.Rep3: mtry=25 
+ Fold10.Rep3: mtry= 5 
- Fold10.Rep3: mtry= 5 
+ Fold10.Rep3: mtry=10 
- Fold10.Rep3: mtry=10 
+ Fold10.Rep3: mtry=15 
- Fold10.Rep3: mtry=15 
+ Fold10.Rep3: mtry=20 
- Fold10.Rep3: mtry=20 
+ Fold10.Rep3: mtry=25 
- Fold10.Rep3: mtry=25 
+ Fold01.Rep4: mtry= 5 
- Fold01.Rep4: mtry= 5 
+ Fold01.Rep4: mtry=10 
- Fold01.Rep4: mtry=10 
+ Fold01.Rep4: mtry=15 
- Fold01.Rep4: mtry=15 
+ Fold01.Rep4: mtry=20 
- Fold01.Rep4: mtry=20 
+ Fold01.Rep4: mtry=25 
- Fold01.Rep4: mtry=25 
+ Fold02.Rep4: mtry= 5 
- Fold02.Rep4: mtry= 5 
+ Fold02.Rep4: mtry=10 
- Fold02.Rep4: mtry=10 
+ Fold02.Rep4: mtry=15 
- Fold02.Rep4: mtry=15 
+ Fold02.Rep4: mtry=20 
- Fold02.Rep4: mtry=20 
+ Fold02.Rep4: mtry=25 
- Fold02.Rep4: mtry=25 
+ Fold03.Rep4: mtry= 5 
- Fold03.Rep4: mtry= 5 
+ Fold03.Rep4: mtry=10 
- Fold03.Rep4: mtry=10 
+ Fold03.Rep4: mtry=15 
- Fold03.Rep4: mtry=15 
+ Fold03.Rep4: mtry=20 
- Fold03.Rep4: mtry=20 
+ Fold03.Rep4: mtry=25 
- Fold03.Rep4: mtry=25 
+ Fold04.Rep4: mtry= 5 
- Fold04.Rep4: mtry= 5 
+ Fold04.Rep4: mtry=10 
- Fold04.Rep4: mtry=10 
+ Fold04.Rep4: mtry=15 
- Fold04.Rep4: mtry=15 
+ Fold04.Rep4: mtry=20 
- Fold04.Rep4: mtry=20 
+ Fold04.Rep4: mtry=25 
- Fold04.Rep4: mtry=25 
+ Fold05.Rep4: mtry= 5 
- Fold05.Rep4: mtry= 5 
+ Fold05.Rep4: mtry=10 
- Fold05.Rep4: mtry=10 
+ Fold05.Rep4: mtry=15 
- Fold05.Rep4: mtry=15 
+ Fold05.Rep4: mtry=20 
- Fold05.Rep4: mtry=20 
+ Fold05.Rep4: mtry=25 
- Fold05.Rep4: mtry=25 
+ Fold06.Rep4: mtry= 5 
- Fold06.Rep4: mtry= 5 
+ Fold06.Rep4: mtry=10 
- Fold06.Rep4: mtry=10 
+ Fold06.Rep4: mtry=15 
- Fold06.Rep4: mtry=15 
+ Fold06.Rep4: mtry=20 
- Fold06.Rep4: mtry=20 
+ Fold06.Rep4: mtry=25 
- Fold06.Rep4: mtry=25 
+ Fold07.Rep4: mtry= 5 
- Fold07.Rep4: mtry= 5 
+ Fold07.Rep4: mtry=10 
- Fold07.Rep4: mtry=10 
+ Fold07.Rep4: mtry=15 
- Fold07.Rep4: mtry=15 
+ Fold07.Rep4: mtry=20 
- Fold07.Rep4: mtry=20 
+ Fold07.Rep4: mtry=25 
- Fold07.Rep4: mtry=25 
+ Fold08.Rep4: mtry= 5 
- Fold08.Rep4: mtry= 5 
+ Fold08.Rep4: mtry=10 
- Fold08.Rep4: mtry=10 
+ Fold08.Rep4: mtry=15 
- Fold08.Rep4: mtry=15 
+ Fold08.Rep4: mtry=20 
- Fold08.Rep4: mtry=20 
+ Fold08.Rep4: mtry=25 
- Fold08.Rep4: mtry=25 
+ Fold09.Rep4: mtry= 5 
- Fold09.Rep4: mtry= 5 
+ Fold09.Rep4: mtry=10 
- Fold09.Rep4: mtry=10 
+ Fold09.Rep4: mtry=15 
- Fold09.Rep4: mtry=15 
+ Fold09.Rep4: mtry=20 
- Fold09.Rep4: mtry=20 
+ Fold09.Rep4: mtry=25 
- Fold09.Rep4: mtry=25 
+ Fold10.Rep4: mtry= 5 
- Fold10.Rep4: mtry= 5 
+ Fold10.Rep4: mtry=10 
- Fold10.Rep4: mtry=10 
+ Fold10.Rep4: mtry=15 
- Fold10.Rep4: mtry=15 
+ Fold10.Rep4: mtry=20 
- Fold10.Rep4: mtry=20 
+ Fold10.Rep4: mtry=25 
- Fold10.Rep4: mtry=25 
+ Fold01.Rep5: mtry= 5 
- Fold01.Rep5: mtry= 5 
+ Fold01.Rep5: mtry=10 
- Fold01.Rep5: mtry=10 
+ Fold01.Rep5: mtry=15 
- Fold01.Rep5: mtry=15 
+ Fold01.Rep5: mtry=20 
- Fold01.Rep5: mtry=20 
+ Fold01.Rep5: mtry=25 
- Fold01.Rep5: mtry=25 
+ Fold02.Rep5: mtry= 5 
- Fold02.Rep5: mtry= 5 
+ Fold02.Rep5: mtry=10 
- Fold02.Rep5: mtry=10 
+ Fold02.Rep5: mtry=15 
- Fold02.Rep5: mtry=15 
+ Fold02.Rep5: mtry=20 
- Fold02.Rep5: mtry=20 
+ Fold02.Rep5: mtry=25 
- Fold02.Rep5: mtry=25 
+ Fold03.Rep5: mtry= 5 
- Fold03.Rep5: mtry= 5 
+ Fold03.Rep5: mtry=10 
- Fold03.Rep5: mtry=10 
+ Fold03.Rep5: mtry=15 
- Fold03.Rep5: mtry=15 
+ Fold03.Rep5: mtry=20 
- Fold03.Rep5: mtry=20 
+ Fold03.Rep5: mtry=25 
- Fold03.Rep5: mtry=25 
+ Fold04.Rep5: mtry= 5 
- Fold04.Rep5: mtry= 5 
+ Fold04.Rep5: mtry=10 
- Fold04.Rep5: mtry=10 
+ Fold04.Rep5: mtry=15 
- Fold04.Rep5: mtry=15 
+ Fold04.Rep5: mtry=20 
- Fold04.Rep5: mtry=20 
+ Fold04.Rep5: mtry=25 
- Fold04.Rep5: mtry=25 
+ Fold05.Rep5: mtry= 5 
- Fold05.Rep5: mtry= 5 
+ Fold05.Rep5: mtry=10 
- Fold05.Rep5: mtry=10 
+ Fold05.Rep5: mtry=15 
- Fold05.Rep5: mtry=15 
+ Fold05.Rep5: mtry=20 
- Fold05.Rep5: mtry=20 
+ Fold05.Rep5: mtry=25 
- Fold05.Rep5: mtry=25 
+ Fold06.Rep5: mtry= 5 
- Fold06.Rep5: mtry= 5 
+ Fold06.Rep5: mtry=10 
- Fold06.Rep5: mtry=10 
+ Fold06.Rep5: mtry=15 
- Fold06.Rep5: mtry=15 
+ Fold06.Rep5: mtry=20 
- Fold06.Rep5: mtry=20 
+ Fold06.Rep5: mtry=25 
- Fold06.Rep5: mtry=25 
+ Fold07.Rep5: mtry= 5 
- Fold07.Rep5: mtry= 5 
+ Fold07.Rep5: mtry=10 
- Fold07.Rep5: mtry=10 
+ Fold07.Rep5: mtry=15 
- Fold07.Rep5: mtry=15 
+ Fold07.Rep5: mtry=20 
- Fold07.Rep5: mtry=20 
+ Fold07.Rep5: mtry=25 
- Fold07.Rep5: mtry=25 
+ Fold08.Rep5: mtry= 5 
- Fold08.Rep5: mtry= 5 
+ Fold08.Rep5: mtry=10 
- Fold08.Rep5: mtry=10 
+ Fold08.Rep5: mtry=15 
- Fold08.Rep5: mtry=15 
+ Fold08.Rep5: mtry=20 
- Fold08.Rep5: mtry=20 
+ Fold08.Rep5: mtry=25 
- Fold08.Rep5: mtry=25 
+ Fold09.Rep5: mtry= 5 
- Fold09.Rep5: mtry= 5 
+ Fold09.Rep5: mtry=10 
- Fold09.Rep5: mtry=10 
+ Fold09.Rep5: mtry=15 
- Fold09.Rep5: mtry=15 
+ Fold09.Rep5: mtry=20 
- Fold09.Rep5: mtry=20 
+ Fold09.Rep5: mtry=25 
- Fold09.Rep5: mtry=25 
+ Fold10.Rep5: mtry= 5 
- Fold10.Rep5: mtry= 5 
+ Fold10.Rep5: mtry=10 
- Fold10.Rep5: mtry=10 
+ Fold10.Rep5: mtry=15 
- Fold10.Rep5: mtry=15 
+ Fold10.Rep5: mtry=20 
- Fold10.Rep5: mtry=20 
+ Fold10.Rep5: mtry=25 
- Fold10.Rep5: mtry=25 
Aggregating results
Selecting tuning parameters
Fitting mtry = 10 on full training set
rfFit.y
Random Forest 

891 samples
 28 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   5    0.8842523  0.7122353  0.8969293
  10    0.8885360  0.7286891  0.8940202
  15    0.8874201  0.7351429  0.8878182
  20    0.8864149  0.7415462  0.8867138
  25    0.8866698  0.7427563  0.8816229

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 10.
plot(rfFit.y)

plot(rfFit.y$finalModel)

densityplot(rfFit.y,pch='|')

predict(rfFit.y,type = 'raw') -> train.rf.Class
predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)

Random Forest (rf) - SMOTE

rfsmoteFit.y
Random Forest 

891 samples
 28 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   5    0.8783818  0.6333782  0.9369966
  10    0.8821756  0.6854286  0.9129697
  15    0.8831274  0.6983025  0.9016498
  20    0.8814043  0.7129580  0.8910976
  25    0.8793606  0.7135462  0.8885455

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 15.
plot(rfsmoteFit.y)

plot(rfsmoteFit.y$finalModel)

densityplot(rfsmoteFit.y,pch='|')

predict(rfsmoteFit.y,type = 'raw') -> train.rfsmoteFit.Class
predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)

Elastinet

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
glmnetGrid <- expand.grid(.alpha = c(0,.1,.2,.4,.6,.8,1),
                          .lambda = seq(0.01,0.2,length.out = 40))
set.seed(1)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
variable 'Survived' is not a factor
glmnetFit <- train(
    x = Dtrain,
    y = train.imp$Survived,
    trControl=ctrl,
    method='glmnet',
    tuneGrid=glmnetGrid
)
1 package is needed for this model and is not installed. (glmnet). Would you like to try to install it now?
1: yes
2: no
1
trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.4/glmnet_2.0-10.tgz'
Content type 'application/x-gzip' length 1468512 bytes (1.4 MB)
==================================================
downloaded 1.4 MB

The downloaded binary packages are in
    /var/folders/08/j649td5x1bgfxk70vw46jg880000gn/T//Rtmpcw7BTF/downloaded_packages
Loading required package: glmnet
Loading required package: Matrix
package ‘Matrix’ was built under R version 3.4.1
Attaching package: ‘Matrix’

The following object is masked from ‘package:tidyr’:

    expand

Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com

Attaching package: ‘foreach’

The following objects are masked from ‘package:purrr’:

    accumulate, when

Loaded glmnet 2.0-10

The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: alpha=0.0, lambda=0.2 
- Fold01.Rep1: alpha=0.0, lambda=0.2 
+ Fold01.Rep1: alpha=0.1, lambda=0.2 
- Fold01.Rep1: alpha=0.1, lambda=0.2 
+ Fold01.Rep1: alpha=0.2, lambda=0.2 
- Fold01.Rep1: alpha=0.2, lambda=0.2 
+ Fold01.Rep1: alpha=0.4, lambda=0.2 
- Fold01.Rep1: alpha=0.4, lambda=0.2 
+ Fold01.Rep1: alpha=0.6, lambda=0.2 
- Fold01.Rep1: alpha=0.6, lambda=0.2 
+ Fold01.Rep1: alpha=0.8, lambda=0.2 
- Fold01.Rep1: alpha=0.8, lambda=0.2 
+ Fold01.Rep1: alpha=1.0, lambda=0.2 
- Fold01.Rep1: alpha=1.0, lambda=0.2 
+ Fold02.Rep1: alpha=0.0, lambda=0.2 
- Fold02.Rep1: alpha=0.0, lambda=0.2 
+ Fold02.Rep1: alpha=0.1, lambda=0.2 
- Fold02.Rep1: alpha=0.1, lambda=0.2 
+ Fold02.Rep1: alpha=0.2, lambda=0.2 
- Fold02.Rep1: alpha=0.2, lambda=0.2 
+ Fold02.Rep1: alpha=0.4, lambda=0.2 
- Fold02.Rep1: alpha=0.4, lambda=0.2 
+ Fold02.Rep1: alpha=0.6, lambda=0.2 
- Fold02.Rep1: alpha=0.6, lambda=0.2 
+ Fold02.Rep1: alpha=0.8, lambda=0.2 
- Fold02.Rep1: alpha=0.8, lambda=0.2 
+ Fold02.Rep1: alpha=1.0, lambda=0.2 
- Fold02.Rep1: alpha=1.0, lambda=0.2 
+ Fold03.Rep1: alpha=0.0, lambda=0.2 
- Fold03.Rep1: alpha=0.0, lambda=0.2 
+ Fold03.Rep1: alpha=0.1, lambda=0.2 
- Fold03.Rep1: alpha=0.1, lambda=0.2 
+ Fold03.Rep1: alpha=0.2, lambda=0.2 
- Fold03.Rep1: alpha=0.2, lambda=0.2 
+ Fold03.Rep1: alpha=0.4, lambda=0.2 
- Fold03.Rep1: alpha=0.4, lambda=0.2 
+ Fold03.Rep1: alpha=0.6, lambda=0.2 
- Fold03.Rep1: alpha=0.6, lambda=0.2 
+ Fold03.Rep1: alpha=0.8, lambda=0.2 
- Fold03.Rep1: alpha=0.8, lambda=0.2 
+ Fold03.Rep1: alpha=1.0, lambda=0.2 
- Fold03.Rep1: alpha=1.0, lambda=0.2 
+ Fold04.Rep1: alpha=0.0, lambda=0.2 
- Fold04.Rep1: alpha=0.0, lambda=0.2 
+ Fold04.Rep1: alpha=0.1, lambda=0.2 
- Fold04.Rep1: alpha=0.1, lambda=0.2 
+ Fold04.Rep1: alpha=0.2, lambda=0.2 
- Fold04.Rep1: alpha=0.2, lambda=0.2 
+ Fold04.Rep1: alpha=0.4, lambda=0.2 
- Fold04.Rep1: alpha=0.4, lambda=0.2 
+ Fold04.Rep1: alpha=0.6, lambda=0.2 
- Fold04.Rep1: alpha=0.6, lambda=0.2 
+ Fold04.Rep1: alpha=0.8, lambda=0.2 
- Fold04.Rep1: alpha=0.8, lambda=0.2 
+ Fold04.Rep1: alpha=1.0, lambda=0.2 
- Fold04.Rep1: alpha=1.0, lambda=0.2 
+ Fold05.Rep1: alpha=0.0, lambda=0.2 
- Fold05.Rep1: alpha=0.0, lambda=0.2 
+ Fold05.Rep1: alpha=0.1, lambda=0.2 
- Fold05.Rep1: alpha=0.1, lambda=0.2 
+ Fold05.Rep1: alpha=0.2, lambda=0.2 
- Fold05.Rep1: alpha=0.2, lambda=0.2 
+ Fold05.Rep1: alpha=0.4, lambda=0.2 
- Fold05.Rep1: alpha=0.4, lambda=0.2 
+ Fold05.Rep1: alpha=0.6, lambda=0.2 
- Fold05.Rep1: alpha=0.6, lambda=0.2 
+ Fold05.Rep1: alpha=0.8, lambda=0.2 
- Fold05.Rep1: alpha=0.8, lambda=0.2 
+ Fold05.Rep1: alpha=1.0, lambda=0.2 
- Fold05.Rep1: alpha=1.0, lambda=0.2 
+ Fold06.Rep1: alpha=0.0, lambda=0.2 
- Fold06.Rep1: alpha=0.0, lambda=0.2 
+ Fold06.Rep1: alpha=0.1, lambda=0.2 
- Fold06.Rep1: alpha=0.1, lambda=0.2 
+ Fold06.Rep1: alpha=0.2, lambda=0.2 
- Fold06.Rep1: alpha=0.2, lambda=0.2 
+ Fold06.Rep1: alpha=0.4, lambda=0.2 
- Fold06.Rep1: alpha=0.4, lambda=0.2 
+ Fold06.Rep1: alpha=0.6, lambda=0.2 
- Fold06.Rep1: alpha=0.6, lambda=0.2 
+ Fold06.Rep1: alpha=0.8, lambda=0.2 
- Fold06.Rep1: alpha=0.8, lambda=0.2 
+ Fold06.Rep1: alpha=1.0, lambda=0.2 
- Fold06.Rep1: alpha=1.0, lambda=0.2 
+ Fold07.Rep1: alpha=0.0, lambda=0.2 
- Fold07.Rep1: alpha=0.0, lambda=0.2 
+ Fold07.Rep1: alpha=0.1, lambda=0.2 
- Fold07.Rep1: alpha=0.1, lambda=0.2 
+ Fold07.Rep1: alpha=0.2, lambda=0.2 
- Fold07.Rep1: alpha=0.2, lambda=0.2 
+ Fold07.Rep1: alpha=0.4, lambda=0.2 
- Fold07.Rep1: alpha=0.4, lambda=0.2 
+ Fold07.Rep1: alpha=0.6, lambda=0.2 
- Fold07.Rep1: alpha=0.6, lambda=0.2 
+ Fold07.Rep1: alpha=0.8, lambda=0.2 
- Fold07.Rep1: alpha=0.8, lambda=0.2 
+ Fold07.Rep1: alpha=1.0, lambda=0.2 
- Fold07.Rep1: alpha=1.0, lambda=0.2 
+ Fold08.Rep1: alpha=0.0, lambda=0.2 
- Fold08.Rep1: alpha=0.0, lambda=0.2 
+ Fold08.Rep1: alpha=0.1, lambda=0.2 
- Fold08.Rep1: alpha=0.1, lambda=0.2 
+ Fold08.Rep1: alpha=0.2, lambda=0.2 
- Fold08.Rep1: alpha=0.2, lambda=0.2 
+ Fold08.Rep1: alpha=0.4, lambda=0.2 
- Fold08.Rep1: alpha=0.4, lambda=0.2 
+ Fold08.Rep1: alpha=0.6, lambda=0.2 
- Fold08.Rep1: alpha=0.6, lambda=0.2 
+ Fold08.Rep1: alpha=0.8, lambda=0.2 
- Fold08.Rep1: alpha=0.8, lambda=0.2 
+ Fold08.Rep1: alpha=1.0, lambda=0.2 
- Fold08.Rep1: alpha=1.0, lambda=0.2 
+ Fold09.Rep1: alpha=0.0, lambda=0.2 
- Fold09.Rep1: alpha=0.0, lambda=0.2 
+ Fold09.Rep1: alpha=0.1, lambda=0.2 
- Fold09.Rep1: alpha=0.1, lambda=0.2 
+ Fold09.Rep1: alpha=0.2, lambda=0.2 
- Fold09.Rep1: alpha=0.2, lambda=0.2 
+ Fold09.Rep1: alpha=0.4, lambda=0.2 
- Fold09.Rep1: alpha=0.4, lambda=0.2 
+ Fold09.Rep1: alpha=0.6, lambda=0.2 
- Fold09.Rep1: alpha=0.6, lambda=0.2 
+ Fold09.Rep1: alpha=0.8, lambda=0.2 
- Fold09.Rep1: alpha=0.8, lambda=0.2 
+ Fold09.Rep1: alpha=1.0, lambda=0.2 
- Fold09.Rep1: alpha=1.0, lambda=0.2 
+ Fold10.Rep1: alpha=0.0, lambda=0.2 
- Fold10.Rep1: alpha=0.0, lambda=0.2 
+ Fold10.Rep1: alpha=0.1, lambda=0.2 
- Fold10.Rep1: alpha=0.1, lambda=0.2 
+ Fold10.Rep1: alpha=0.2, lambda=0.2 
- Fold10.Rep1: alpha=0.2, lambda=0.2 
+ Fold10.Rep1: alpha=0.4, lambda=0.2 
- Fold10.Rep1: alpha=0.4, lambda=0.2 
+ Fold10.Rep1: alpha=0.6, lambda=0.2 
- Fold10.Rep1: alpha=0.6, lambda=0.2 
+ Fold10.Rep1: alpha=0.8, lambda=0.2 
- Fold10.Rep1: alpha=0.8, lambda=0.2 
+ Fold10.Rep1: alpha=1.0, lambda=0.2 
- Fold10.Rep1: alpha=1.0, lambda=0.2 
+ Fold01.Rep2: alpha=0.0, lambda=0.2 
- Fold01.Rep2: alpha=0.0, lambda=0.2 
+ Fold01.Rep2: alpha=0.1, lambda=0.2 
- Fold01.Rep2: alpha=0.1, lambda=0.2 
+ Fold01.Rep2: alpha=0.2, lambda=0.2 
- Fold01.Rep2: alpha=0.2, lambda=0.2 
+ Fold01.Rep2: alpha=0.4, lambda=0.2 
- Fold01.Rep2: alpha=0.4, lambda=0.2 
+ Fold01.Rep2: alpha=0.6, lambda=0.2 
- Fold01.Rep2: alpha=0.6, lambda=0.2 
+ Fold01.Rep2: alpha=0.8, lambda=0.2 
- Fold01.Rep2: alpha=0.8, lambda=0.2 
+ Fold01.Rep2: alpha=1.0, lambda=0.2 
- Fold01.Rep2: alpha=1.0, lambda=0.2 
+ Fold02.Rep2: alpha=0.0, lambda=0.2 
- Fold02.Rep2: alpha=0.0, lambda=0.2 
+ Fold02.Rep2: alpha=0.1, lambda=0.2 
- Fold02.Rep2: alpha=0.1, lambda=0.2 
+ Fold02.Rep2: alpha=0.2, lambda=0.2 
- Fold02.Rep2: alpha=0.2, lambda=0.2 
+ Fold02.Rep2: alpha=0.4, lambda=0.2 
- Fold02.Rep2: alpha=0.4, lambda=0.2 
+ Fold02.Rep2: alpha=0.6, lambda=0.2 
- Fold02.Rep2: alpha=0.6, lambda=0.2 
+ Fold02.Rep2: alpha=0.8, lambda=0.2 
- Fold02.Rep2: alpha=0.8, lambda=0.2 
+ Fold02.Rep2: alpha=1.0, lambda=0.2 
- Fold02.Rep2: alpha=1.0, lambda=0.2 
+ Fold03.Rep2: alpha=0.0, lambda=0.2 
- Fold03.Rep2: alpha=0.0, lambda=0.2 
+ Fold03.Rep2: alpha=0.1, lambda=0.2 
- Fold03.Rep2: alpha=0.1, lambda=0.2 
+ Fold03.Rep2: alpha=0.2, lambda=0.2 
- Fold03.Rep2: alpha=0.2, lambda=0.2 
+ Fold03.Rep2: alpha=0.4, lambda=0.2 
- Fold03.Rep2: alpha=0.4, lambda=0.2 
+ Fold03.Rep2: alpha=0.6, lambda=0.2 
- Fold03.Rep2: alpha=0.6, lambda=0.2 
+ Fold03.Rep2: alpha=0.8, lambda=0.2 
- Fold03.Rep2: alpha=0.8, lambda=0.2 
+ Fold03.Rep2: alpha=1.0, lambda=0.2 
- Fold03.Rep2: alpha=1.0, lambda=0.2 
+ Fold04.Rep2: alpha=0.0, lambda=0.2 
- Fold04.Rep2: alpha=0.0, lambda=0.2 
+ Fold04.Rep2: alpha=0.1, lambda=0.2 
- Fold04.Rep2: alpha=0.1, lambda=0.2 
+ Fold04.Rep2: alpha=0.2, lambda=0.2 
- Fold04.Rep2: alpha=0.2, lambda=0.2 
+ Fold04.Rep2: alpha=0.4, lambda=0.2 
- Fold04.Rep2: alpha=0.4, lambda=0.2 
+ Fold04.Rep2: alpha=0.6, lambda=0.2 
- Fold04.Rep2: alpha=0.6, lambda=0.2 
+ Fold04.Rep2: alpha=0.8, lambda=0.2 
- Fold04.Rep2: alpha=0.8, lambda=0.2 
+ Fold04.Rep2: alpha=1.0, lambda=0.2 
- Fold04.Rep2: alpha=1.0, lambda=0.2 
+ Fold05.Rep2: alpha=0.0, lambda=0.2 
- Fold05.Rep2: alpha=0.0, lambda=0.2 
+ Fold05.Rep2: alpha=0.1, lambda=0.2 
- Fold05.Rep2: alpha=0.1, lambda=0.2 
+ Fold05.Rep2: alpha=0.2, lambda=0.2 
- Fold05.Rep2: alpha=0.2, lambda=0.2 
+ Fold05.Rep2: alpha=0.4, lambda=0.2 
- Fold05.Rep2: alpha=0.4, lambda=0.2 
+ Fold05.Rep2: alpha=0.6, lambda=0.2 
- Fold05.Rep2: alpha=0.6, lambda=0.2 
+ Fold05.Rep2: alpha=0.8, lambda=0.2 
- Fold05.Rep2: alpha=0.8, lambda=0.2 
+ Fold05.Rep2: alpha=1.0, lambda=0.2 
- Fold05.Rep2: alpha=1.0, lambda=0.2 
+ Fold06.Rep2: alpha=0.0, lambda=0.2 
- Fold06.Rep2: alpha=0.0, lambda=0.2 
+ Fold06.Rep2: alpha=0.1, lambda=0.2 
- Fold06.Rep2: alpha=0.1, lambda=0.2 
+ Fold06.Rep2: alpha=0.2, lambda=0.2 
- Fold06.Rep2: alpha=0.2, lambda=0.2 
+ Fold06.Rep2: alpha=0.4, lambda=0.2 
- Fold06.Rep2: alpha=0.4, lambda=0.2 
+ Fold06.Rep2: alpha=0.6, lambda=0.2 
- Fold06.Rep2: alpha=0.6, lambda=0.2 
+ Fold06.Rep2: alpha=0.8, lambda=0.2 
- Fold06.Rep2: alpha=0.8, lambda=0.2 
+ Fold06.Rep2: alpha=1.0, lambda=0.2 
- Fold06.Rep2: alpha=1.0, lambda=0.2 
+ Fold07.Rep2: alpha=0.0, lambda=0.2 
- Fold07.Rep2: alpha=0.0, lambda=0.2 
+ Fold07.Rep2: alpha=0.1, lambda=0.2 
- Fold07.Rep2: alpha=0.1, lambda=0.2 
+ Fold07.Rep2: alpha=0.2, lambda=0.2 
- Fold07.Rep2: alpha=0.2, lambda=0.2 
+ Fold07.Rep2: alpha=0.4, lambda=0.2 
- Fold07.Rep2: alpha=0.4, lambda=0.2 
+ Fold07.Rep2: alpha=0.6, lambda=0.2 
- Fold07.Rep2: alpha=0.6, lambda=0.2 
+ Fold07.Rep2: alpha=0.8, lambda=0.2 
- Fold07.Rep2: alpha=0.8, lambda=0.2 
+ Fold07.Rep2: alpha=1.0, lambda=0.2 
- Fold07.Rep2: alpha=1.0, lambda=0.2 
+ Fold08.Rep2: alpha=0.0, lambda=0.2 
- Fold08.Rep2: alpha=0.0, lambda=0.2 
+ Fold08.Rep2: alpha=0.1, lambda=0.2 
- Fold08.Rep2: alpha=0.1, lambda=0.2 
+ Fold08.Rep2: alpha=0.2, lambda=0.2 
- Fold08.Rep2: alpha=0.2, lambda=0.2 
+ Fold08.Rep2: alpha=0.4, lambda=0.2 
- Fold08.Rep2: alpha=0.4, lambda=0.2 
+ Fold08.Rep2: alpha=0.6, lambda=0.2 
- Fold08.Rep2: alpha=0.6, lambda=0.2 
+ Fold08.Rep2: alpha=0.8, lambda=0.2 
- Fold08.Rep2: alpha=0.8, lambda=0.2 
+ Fold08.Rep2: alpha=1.0, lambda=0.2 
- Fold08.Rep2: alpha=1.0, lambda=0.2 
+ Fold09.Rep2: alpha=0.0, lambda=0.2 
- Fold09.Rep2: alpha=0.0, lambda=0.2 
+ Fold09.Rep2: alpha=0.1, lambda=0.2 
- Fold09.Rep2: alpha=0.1, lambda=0.2 
+ Fold09.Rep2: alpha=0.2, lambda=0.2 
- Fold09.Rep2: alpha=0.2, lambda=0.2 
+ Fold09.Rep2: alpha=0.4, lambda=0.2 
- Fold09.Rep2: alpha=0.4, lambda=0.2 
+ Fold09.Rep2: alpha=0.6, lambda=0.2 
- Fold09.Rep2: alpha=0.6, lambda=0.2 
+ Fold09.Rep2: alpha=0.8, lambda=0.2 
- Fold09.Rep2: alpha=0.8, lambda=0.2 
+ Fold09.Rep2: alpha=1.0, lambda=0.2 
- Fold09.Rep2: alpha=1.0, lambda=0.2 
+ Fold10.Rep2: alpha=0.0, lambda=0.2 
- Fold10.Rep2: alpha=0.0, lambda=0.2 
+ Fold10.Rep2: alpha=0.1, lambda=0.2 
- Fold10.Rep2: alpha=0.1, lambda=0.2 
+ Fold10.Rep2: alpha=0.2, lambda=0.2 
- Fold10.Rep2: alpha=0.2, lambda=0.2 
+ Fold10.Rep2: alpha=0.4, lambda=0.2 
- Fold10.Rep2: alpha=0.4, lambda=0.2 
+ Fold10.Rep2: alpha=0.6, lambda=0.2 
- Fold10.Rep2: alpha=0.6, lambda=0.2 
+ Fold10.Rep2: alpha=0.8, lambda=0.2 
- Fold10.Rep2: alpha=0.8, lambda=0.2 
+ Fold10.Rep2: alpha=1.0, lambda=0.2 
- Fold10.Rep2: alpha=1.0, lambda=0.2 
+ Fold01.Rep3: alpha=0.0, lambda=0.2 
- Fold01.Rep3: alpha=0.0, lambda=0.2 
+ Fold01.Rep3: alpha=0.1, lambda=0.2 
- Fold01.Rep3: alpha=0.1, lambda=0.2 
+ Fold01.Rep3: alpha=0.2, lambda=0.2 
- Fold01.Rep3: alpha=0.2, lambda=0.2 
+ Fold01.Rep3: alpha=0.4, lambda=0.2 
- Fold01.Rep3: alpha=0.4, lambda=0.2 
+ Fold01.Rep3: alpha=0.6, lambda=0.2 
- Fold01.Rep3: alpha=0.6, lambda=0.2 
+ Fold01.Rep3: alpha=0.8, lambda=0.2 
- Fold01.Rep3: alpha=0.8, lambda=0.2 
+ Fold01.Rep3: alpha=1.0, lambda=0.2 
- Fold01.Rep3: alpha=1.0, lambda=0.2 
+ Fold02.Rep3: alpha=0.0, lambda=0.2 
- Fold02.Rep3: alpha=0.0, lambda=0.2 
+ Fold02.Rep3: alpha=0.1, lambda=0.2 
- Fold02.Rep3: alpha=0.1, lambda=0.2 
+ Fold02.Rep3: alpha=0.2, lambda=0.2 
- Fold02.Rep3: alpha=0.2, lambda=0.2 
+ Fold02.Rep3: alpha=0.4, lambda=0.2 
- Fold02.Rep3: alpha=0.4, lambda=0.2 
+ Fold02.Rep3: alpha=0.6, lambda=0.2 
- Fold02.Rep3: alpha=0.6, lambda=0.2 
+ Fold02.Rep3: alpha=0.8, lambda=0.2 
- Fold02.Rep3: alpha=0.8, lambda=0.2 
+ Fold02.Rep3: alpha=1.0, lambda=0.2 
- Fold02.Rep3: alpha=1.0, lambda=0.2 
+ Fold03.Rep3: alpha=0.0, lambda=0.2 
- Fold03.Rep3: alpha=0.0, lambda=0.2 
+ Fold03.Rep3: alpha=0.1, lambda=0.2 
- Fold03.Rep3: alpha=0.1, lambda=0.2 
+ Fold03.Rep3: alpha=0.2, lambda=0.2 
- Fold03.Rep3: alpha=0.2, lambda=0.2 
+ Fold03.Rep3: alpha=0.4, lambda=0.2 
- Fold03.Rep3: alpha=0.4, lambda=0.2 
+ Fold03.Rep3: alpha=0.6, lambda=0.2 
- Fold03.Rep3: alpha=0.6, lambda=0.2 
+ Fold03.Rep3: alpha=0.8, lambda=0.2 
- Fold03.Rep3: alpha=0.8, lambda=0.2 
+ Fold03.Rep3: alpha=1.0, lambda=0.2 
- Fold03.Rep3: alpha=1.0, lambda=0.2 
+ Fold04.Rep3: alpha=0.0, lambda=0.2 
- Fold04.Rep3: alpha=0.0, lambda=0.2 
+ Fold04.Rep3: alpha=0.1, lambda=0.2 
- Fold04.Rep3: alpha=0.1, lambda=0.2 
+ Fold04.Rep3: alpha=0.2, lambda=0.2 
- Fold04.Rep3: alpha=0.2, lambda=0.2 
+ Fold04.Rep3: alpha=0.4, lambda=0.2 
- Fold04.Rep3: alpha=0.4, lambda=0.2 
+ Fold04.Rep3: alpha=0.6, lambda=0.2 
- Fold04.Rep3: alpha=0.6, lambda=0.2 
+ Fold04.Rep3: alpha=0.8, lambda=0.2 
- Fold04.Rep3: alpha=0.8, lambda=0.2 
+ Fold04.Rep3: alpha=1.0, lambda=0.2 
- Fold04.Rep3: alpha=1.0, lambda=0.2 
+ Fold05.Rep3: alpha=0.0, lambda=0.2 
- Fold05.Rep3: alpha=0.0, lambda=0.2 
+ Fold05.Rep3: alpha=0.1, lambda=0.2 
- Fold05.Rep3: alpha=0.1, lambda=0.2 
+ Fold05.Rep3: alpha=0.2, lambda=0.2 
- Fold05.Rep3: alpha=0.2, lambda=0.2 
+ Fold05.Rep3: alpha=0.4, lambda=0.2 
- Fold05.Rep3: alpha=0.4, lambda=0.2 
+ Fold05.Rep3: alpha=0.6, lambda=0.2 
- Fold05.Rep3: alpha=0.6, lambda=0.2 
+ Fold05.Rep3: alpha=0.8, lambda=0.2 
- Fold05.Rep3: alpha=0.8, lambda=0.2 
+ Fold05.Rep3: alpha=1.0, lambda=0.2 
- Fold05.Rep3: alpha=1.0, lambda=0.2 
+ Fold06.Rep3: alpha=0.0, lambda=0.2 
- Fold06.Rep3: alpha=0.0, lambda=0.2 
+ Fold06.Rep3: alpha=0.1, lambda=0.2 
- Fold06.Rep3: alpha=0.1, lambda=0.2 
+ Fold06.Rep3: alpha=0.2, lambda=0.2 
- Fold06.Rep3: alpha=0.2, lambda=0.2 
+ Fold06.Rep3: alpha=0.4, lambda=0.2 
- Fold06.Rep3: alpha=0.4, lambda=0.2 
+ Fold06.Rep3: alpha=0.6, lambda=0.2 
- Fold06.Rep3: alpha=0.6, lambda=0.2 
+ Fold06.Rep3: alpha=0.8, lambda=0.2 
- Fold06.Rep3: alpha=0.8, lambda=0.2 
+ Fold06.Rep3: alpha=1.0, lambda=0.2 
- Fold06.Rep3: alpha=1.0, lambda=0.2 
+ Fold07.Rep3: alpha=0.0, lambda=0.2 
- Fold07.Rep3: alpha=0.0, lambda=0.2 
+ Fold07.Rep3: alpha=0.1, lambda=0.2 
- Fold07.Rep3: alpha=0.1, lambda=0.2 
+ Fold07.Rep3: alpha=0.2, lambda=0.2 
- Fold07.Rep3: alpha=0.2, lambda=0.2 
+ Fold07.Rep3: alpha=0.4, lambda=0.2 
- Fold07.Rep3: alpha=0.4, lambda=0.2 
+ Fold07.Rep3: alpha=0.6, lambda=0.2 
- Fold07.Rep3: alpha=0.6, lambda=0.2 
+ Fold07.Rep3: alpha=0.8, lambda=0.2 
- Fold07.Rep3: alpha=0.8, lambda=0.2 
+ Fold07.Rep3: alpha=1.0, lambda=0.2 
- Fold07.Rep3: alpha=1.0, lambda=0.2 
+ Fold08.Rep3: alpha=0.0, lambda=0.2 
- Fold08.Rep3: alpha=0.0, lambda=0.2 
+ Fold08.Rep3: alpha=0.1, lambda=0.2 
- Fold08.Rep3: alpha=0.1, lambda=0.2 
+ Fold08.Rep3: alpha=0.2, lambda=0.2 
- Fold08.Rep3: alpha=0.2, lambda=0.2 
+ Fold08.Rep3: alpha=0.4, lambda=0.2 
- Fold08.Rep3: alpha=0.4, lambda=0.2 
+ Fold08.Rep3: alpha=0.6, lambda=0.2 
- Fold08.Rep3: alpha=0.6, lambda=0.2 
+ Fold08.Rep3: alpha=0.8, lambda=0.2 
- Fold08.Rep3: alpha=0.8, lambda=0.2 
+ Fold08.Rep3: alpha=1.0, lambda=0.2 
- Fold08.Rep3: alpha=1.0, lambda=0.2 
+ Fold09.Rep3: alpha=0.0, lambda=0.2 
- Fold09.Rep3: alpha=0.0, lambda=0.2 
+ Fold09.Rep3: alpha=0.1, lambda=0.2 
- Fold09.Rep3: alpha=0.1, lambda=0.2 
+ Fold09.Rep3: alpha=0.2, lambda=0.2 
- Fold09.Rep3: alpha=0.2, lambda=0.2 
+ Fold09.Rep3: alpha=0.4, lambda=0.2 
- Fold09.Rep3: alpha=0.4, lambda=0.2 
+ Fold09.Rep3: alpha=0.6, lambda=0.2 
- Fold09.Rep3: alpha=0.6, lambda=0.2 
+ Fold09.Rep3: alpha=0.8, lambda=0.2 
- Fold09.Rep3: alpha=0.8, lambda=0.2 
+ Fold09.Rep3: alpha=1.0, lambda=0.2 
- Fold09.Rep3: alpha=1.0, lambda=0.2 
+ Fold10.Rep3: alpha=0.0, lambda=0.2 
- Fold10.Rep3: alpha=0.0, lambda=0.2 
+ Fold10.Rep3: alpha=0.1, lambda=0.2 
- Fold10.Rep3: alpha=0.1, lambda=0.2 
+ Fold10.Rep3: alpha=0.2, lambda=0.2 
- Fold10.Rep3: alpha=0.2, lambda=0.2 
+ Fold10.Rep3: alpha=0.4, lambda=0.2 
- Fold10.Rep3: alpha=0.4, lambda=0.2 
+ Fold10.Rep3: alpha=0.6, lambda=0.2 
- Fold10.Rep3: alpha=0.6, lambda=0.2 
+ Fold10.Rep3: alpha=0.8, lambda=0.2 
- Fold10.Rep3: alpha=0.8, lambda=0.2 
+ Fold10.Rep3: alpha=1.0, lambda=0.2 
- Fold10.Rep3: alpha=1.0, lambda=0.2 
+ Fold01.Rep4: alpha=0.0, lambda=0.2 
- Fold01.Rep4: alpha=0.0, lambda=0.2 
+ Fold01.Rep4: alpha=0.1, lambda=0.2 
- Fold01.Rep4: alpha=0.1, lambda=0.2 
+ Fold01.Rep4: alpha=0.2, lambda=0.2 
- Fold01.Rep4: alpha=0.2, lambda=0.2 
+ Fold01.Rep4: alpha=0.4, lambda=0.2 
- Fold01.Rep4: alpha=0.4, lambda=0.2 
+ Fold01.Rep4: alpha=0.6, lambda=0.2 
- Fold01.Rep4: alpha=0.6, lambda=0.2 
+ Fold01.Rep4: alpha=0.8, lambda=0.2 
- Fold01.Rep4: alpha=0.8, lambda=0.2 
+ Fold01.Rep4: alpha=1.0, lambda=0.2 
- Fold01.Rep4: alpha=1.0, lambda=0.2 
+ Fold02.Rep4: alpha=0.0, lambda=0.2 
- Fold02.Rep4: alpha=0.0, lambda=0.2 
+ Fold02.Rep4: alpha=0.1, lambda=0.2 
- Fold02.Rep4: alpha=0.1, lambda=0.2 
+ Fold02.Rep4: alpha=0.2, lambda=0.2 
- Fold02.Rep4: alpha=0.2, lambda=0.2 
+ Fold02.Rep4: alpha=0.4, lambda=0.2 
- Fold02.Rep4: alpha=0.4, lambda=0.2 
+ Fold02.Rep4: alpha=0.6, lambda=0.2 
- Fold02.Rep4: alpha=0.6, lambda=0.2 
+ Fold02.Rep4: alpha=0.8, lambda=0.2 
- Fold02.Rep4: alpha=0.8, lambda=0.2 
+ Fold02.Rep4: alpha=1.0, lambda=0.2 
- Fold02.Rep4: alpha=1.0, lambda=0.2 
+ Fold03.Rep4: alpha=0.0, lambda=0.2 
- Fold03.Rep4: alpha=0.0, lambda=0.2 
+ Fold03.Rep4: alpha=0.1, lambda=0.2 
- Fold03.Rep4: alpha=0.1, lambda=0.2 
+ Fold03.Rep4: alpha=0.2, lambda=0.2 
- Fold03.Rep4: alpha=0.2, lambda=0.2 
+ Fold03.Rep4: alpha=0.4, lambda=0.2 
- Fold03.Rep4: alpha=0.4, lambda=0.2 
+ Fold03.Rep4: alpha=0.6, lambda=0.2 
- Fold03.Rep4: alpha=0.6, lambda=0.2 
+ Fold03.Rep4: alpha=0.8, lambda=0.2 
- Fold03.Rep4: alpha=0.8, lambda=0.2 
+ Fold03.Rep4: alpha=1.0, lambda=0.2 
- Fold03.Rep4: alpha=1.0, lambda=0.2 
+ Fold04.Rep4: alpha=0.0, lambda=0.2 
- Fold04.Rep4: alpha=0.0, lambda=0.2 
+ Fold04.Rep4: alpha=0.1, lambda=0.2 
- Fold04.Rep4: alpha=0.1, lambda=0.2 
+ Fold04.Rep4: alpha=0.2, lambda=0.2 
- Fold04.Rep4: alpha=0.2, lambda=0.2 
+ Fold04.Rep4: alpha=0.4, lambda=0.2 
- Fold04.Rep4: alpha=0.4, lambda=0.2 
+ Fold04.Rep4: alpha=0.6, lambda=0.2 
- Fold04.Rep4: alpha=0.6, lambda=0.2 
+ Fold04.Rep4: alpha=0.8, lambda=0.2 
- Fold04.Rep4: alpha=0.8, lambda=0.2 
+ Fold04.Rep4: alpha=1.0, lambda=0.2 
- Fold04.Rep4: alpha=1.0, lambda=0.2 
+ Fold05.Rep4: alpha=0.0, lambda=0.2 
- Fold05.Rep4: alpha=0.0, lambda=0.2 
+ Fold05.Rep4: alpha=0.1, lambda=0.2 
- Fold05.Rep4: alpha=0.1, lambda=0.2 
+ Fold05.Rep4: alpha=0.2, lambda=0.2 
- Fold05.Rep4: alpha=0.2, lambda=0.2 
+ Fold05.Rep4: alpha=0.4, lambda=0.2 
- Fold05.Rep4: alpha=0.4, lambda=0.2 
+ Fold05.Rep4: alpha=0.6, lambda=0.2 
- Fold05.Rep4: alpha=0.6, lambda=0.2 
+ Fold05.Rep4: alpha=0.8, lambda=0.2 
- Fold05.Rep4: alpha=0.8, lambda=0.2 
+ Fold05.Rep4: alpha=1.0, lambda=0.2 
- Fold05.Rep4: alpha=1.0, lambda=0.2 
+ Fold06.Rep4: alpha=0.0, lambda=0.2 
- Fold06.Rep4: alpha=0.0, lambda=0.2 
+ Fold06.Rep4: alpha=0.1, lambda=0.2 
- Fold06.Rep4: alpha=0.1, lambda=0.2 
+ Fold06.Rep4: alpha=0.2, lambda=0.2 
- Fold06.Rep4: alpha=0.2, lambda=0.2 
+ Fold06.Rep4: alpha=0.4, lambda=0.2 
- Fold06.Rep4: alpha=0.4, lambda=0.2 
+ Fold06.Rep4: alpha=0.6, lambda=0.2 
- Fold06.Rep4: alpha=0.6, lambda=0.2 
+ Fold06.Rep4: alpha=0.8, lambda=0.2 
- Fold06.Rep4: alpha=0.8, lambda=0.2 
+ Fold06.Rep4: alpha=1.0, lambda=0.2 
- Fold06.Rep4: alpha=1.0, lambda=0.2 
+ Fold07.Rep4: alpha=0.0, lambda=0.2 
- Fold07.Rep4: alpha=0.0, lambda=0.2 
+ Fold07.Rep4: alpha=0.1, lambda=0.2 
- Fold07.Rep4: alpha=0.1, lambda=0.2 
+ Fold07.Rep4: alpha=0.2, lambda=0.2 
- Fold07.Rep4: alpha=0.2, lambda=0.2 
+ Fold07.Rep4: alpha=0.4, lambda=0.2 
- Fold07.Rep4: alpha=0.4, lambda=0.2 
+ Fold07.Rep4: alpha=0.6, lambda=0.2 
- Fold07.Rep4: alpha=0.6, lambda=0.2 
+ Fold07.Rep4: alpha=0.8, lambda=0.2 
- Fold07.Rep4: alpha=0.8, lambda=0.2 
+ Fold07.Rep4: alpha=1.0, lambda=0.2 
- Fold07.Rep4: alpha=1.0, lambda=0.2 
+ Fold08.Rep4: alpha=0.0, lambda=0.2 
- Fold08.Rep4: alpha=0.0, lambda=0.2 
+ Fold08.Rep4: alpha=0.1, lambda=0.2 
- Fold08.Rep4: alpha=0.1, lambda=0.2 
+ Fold08.Rep4: alpha=0.2, lambda=0.2 
- Fold08.Rep4: alpha=0.2, lambda=0.2 
+ Fold08.Rep4: alpha=0.4, lambda=0.2 
- Fold08.Rep4: alpha=0.4, lambda=0.2 
+ Fold08.Rep4: alpha=0.6, lambda=0.2 
- Fold08.Rep4: alpha=0.6, lambda=0.2 
+ Fold08.Rep4: alpha=0.8, lambda=0.2 
- Fold08.Rep4: alpha=0.8, lambda=0.2 
+ Fold08.Rep4: alpha=1.0, lambda=0.2 
- Fold08.Rep4: alpha=1.0, lambda=0.2 
+ Fold09.Rep4: alpha=0.0, lambda=0.2 
- Fold09.Rep4: alpha=0.0, lambda=0.2 
+ Fold09.Rep4: alpha=0.1, lambda=0.2 
- Fold09.Rep4: alpha=0.1, lambda=0.2 
+ Fold09.Rep4: alpha=0.2, lambda=0.2 
- Fold09.Rep4: alpha=0.2, lambda=0.2 
+ Fold09.Rep4: alpha=0.4, lambda=0.2 
- Fold09.Rep4: alpha=0.4, lambda=0.2 
+ Fold09.Rep4: alpha=0.6, lambda=0.2 
- Fold09.Rep4: alpha=0.6, lambda=0.2 
+ Fold09.Rep4: alpha=0.8, lambda=0.2 
- Fold09.Rep4: alpha=0.8, lambda=0.2 
+ Fold09.Rep4: alpha=1.0, lambda=0.2 
- Fold09.Rep4: alpha=1.0, lambda=0.2 
+ Fold10.Rep4: alpha=0.0, lambda=0.2 
- Fold10.Rep4: alpha=0.0, lambda=0.2 
+ Fold10.Rep4: alpha=0.1, lambda=0.2 
- Fold10.Rep4: alpha=0.1, lambda=0.2 
+ Fold10.Rep4: alpha=0.2, lambda=0.2 
- Fold10.Rep4: alpha=0.2, lambda=0.2 
+ Fold10.Rep4: alpha=0.4, lambda=0.2 
- Fold10.Rep4: alpha=0.4, lambda=0.2 
+ Fold10.Rep4: alpha=0.6, lambda=0.2 
- Fold10.Rep4: alpha=0.6, lambda=0.2 
+ Fold10.Rep4: alpha=0.8, lambda=0.2 
- Fold10.Rep4: alpha=0.8, lambda=0.2 
+ Fold10.Rep4: alpha=1.0, lambda=0.2 
- Fold10.Rep4: alpha=1.0, lambda=0.2 
+ Fold01.Rep5: alpha=0.0, lambda=0.2 
- Fold01.Rep5: alpha=0.0, lambda=0.2 
+ Fold01.Rep5: alpha=0.1, lambda=0.2 
- Fold01.Rep5: alpha=0.1, lambda=0.2 
+ Fold01.Rep5: alpha=0.2, lambda=0.2 
- Fold01.Rep5: alpha=0.2, lambda=0.2 
+ Fold01.Rep5: alpha=0.4, lambda=0.2 
- Fold01.Rep5: alpha=0.4, lambda=0.2 
+ Fold01.Rep5: alpha=0.6, lambda=0.2 
- Fold01.Rep5: alpha=0.6, lambda=0.2 
+ Fold01.Rep5: alpha=0.8, lambda=0.2 
- Fold01.Rep5: alpha=0.8, lambda=0.2 
+ Fold01.Rep5: alpha=1.0, lambda=0.2 
- Fold01.Rep5: alpha=1.0, lambda=0.2 
+ Fold02.Rep5: alpha=0.0, lambda=0.2 
- Fold02.Rep5: alpha=0.0, lambda=0.2 
+ Fold02.Rep5: alpha=0.1, lambda=0.2 
- Fold02.Rep5: alpha=0.1, lambda=0.2 
+ Fold02.Rep5: alpha=0.2, lambda=0.2 
- Fold02.Rep5: alpha=0.2, lambda=0.2 
+ Fold02.Rep5: alpha=0.4, lambda=0.2 
- Fold02.Rep5: alpha=0.4, lambda=0.2 
+ Fold02.Rep5: alpha=0.6, lambda=0.2 
- Fold02.Rep5: alpha=0.6, lambda=0.2 
+ Fold02.Rep5: alpha=0.8, lambda=0.2 
- Fold02.Rep5: alpha=0.8, lambda=0.2 
+ Fold02.Rep5: alpha=1.0, lambda=0.2 
- Fold02.Rep5: alpha=1.0, lambda=0.2 
+ Fold03.Rep5: alpha=0.0, lambda=0.2 
- Fold03.Rep5: alpha=0.0, lambda=0.2 
+ Fold03.Rep5: alpha=0.1, lambda=0.2 
- Fold03.Rep5: alpha=0.1, lambda=0.2 
+ Fold03.Rep5: alpha=0.2, lambda=0.2 
- Fold03.Rep5: alpha=0.2, lambda=0.2 
+ Fold03.Rep5: alpha=0.4, lambda=0.2 
- Fold03.Rep5: alpha=0.4, lambda=0.2 
+ Fold03.Rep5: alpha=0.6, lambda=0.2 
- Fold03.Rep5: alpha=0.6, lambda=0.2 
+ Fold03.Rep5: alpha=0.8, lambda=0.2 
- Fold03.Rep5: alpha=0.8, lambda=0.2 
+ Fold03.Rep5: alpha=1.0, lambda=0.2 
- Fold03.Rep5: alpha=1.0, lambda=0.2 
+ Fold04.Rep5: alpha=0.0, lambda=0.2 
- Fold04.Rep5: alpha=0.0, lambda=0.2 
+ Fold04.Rep5: alpha=0.1, lambda=0.2 
- Fold04.Rep5: alpha=0.1, lambda=0.2 
+ Fold04.Rep5: alpha=0.2, lambda=0.2 
- Fold04.Rep5: alpha=0.2, lambda=0.2 
+ Fold04.Rep5: alpha=0.4, lambda=0.2 
- Fold04.Rep5: alpha=0.4, lambda=0.2 
+ Fold04.Rep5: alpha=0.6, lambda=0.2 
- Fold04.Rep5: alpha=0.6, lambda=0.2 
+ Fold04.Rep5: alpha=0.8, lambda=0.2 
- Fold04.Rep5: alpha=0.8, lambda=0.2 
+ Fold04.Rep5: alpha=1.0, lambda=0.2 
- Fold04.Rep5: alpha=1.0, lambda=0.2 
+ Fold05.Rep5: alpha=0.0, lambda=0.2 
- Fold05.Rep5: alpha=0.0, lambda=0.2 
+ Fold05.Rep5: alpha=0.1, lambda=0.2 
- Fold05.Rep5: alpha=0.1, lambda=0.2 
+ Fold05.Rep5: alpha=0.2, lambda=0.2 
- Fold05.Rep5: alpha=0.2, lambda=0.2 
+ Fold05.Rep5: alpha=0.4, lambda=0.2 
- Fold05.Rep5: alpha=0.4, lambda=0.2 
+ Fold05.Rep5: alpha=0.6, lambda=0.2 
- Fold05.Rep5: alpha=0.6, lambda=0.2 
+ Fold05.Rep5: alpha=0.8, lambda=0.2 
- Fold05.Rep5: alpha=0.8, lambda=0.2 
+ Fold05.Rep5: alpha=1.0, lambda=0.2 
- Fold05.Rep5: alpha=1.0, lambda=0.2 
+ Fold06.Rep5: alpha=0.0, lambda=0.2 
- Fold06.Rep5: alpha=0.0, lambda=0.2 
+ Fold06.Rep5: alpha=0.1, lambda=0.2 
- Fold06.Rep5: alpha=0.1, lambda=0.2 
+ Fold06.Rep5: alpha=0.2, lambda=0.2 
- Fold06.Rep5: alpha=0.2, lambda=0.2 
+ Fold06.Rep5: alpha=0.4, lambda=0.2 
- Fold06.Rep5: alpha=0.4, lambda=0.2 
+ Fold06.Rep5: alpha=0.6, lambda=0.2 
- Fold06.Rep5: alpha=0.6, lambda=0.2 
+ Fold06.Rep5: alpha=0.8, lambda=0.2 
- Fold06.Rep5: alpha=0.8, lambda=0.2 
+ Fold06.Rep5: alpha=1.0, lambda=0.2 
- Fold06.Rep5: alpha=1.0, lambda=0.2 
+ Fold07.Rep5: alpha=0.0, lambda=0.2 
- Fold07.Rep5: alpha=0.0, lambda=0.2 
+ Fold07.Rep5: alpha=0.1, lambda=0.2 
- Fold07.Rep5: alpha=0.1, lambda=0.2 
+ Fold07.Rep5: alpha=0.2, lambda=0.2 
- Fold07.Rep5: alpha=0.2, lambda=0.2 
+ Fold07.Rep5: alpha=0.4, lambda=0.2 
- Fold07.Rep5: alpha=0.4, lambda=0.2 
+ Fold07.Rep5: alpha=0.6, lambda=0.2 
- Fold07.Rep5: alpha=0.6, lambda=0.2 
+ Fold07.Rep5: alpha=0.8, lambda=0.2 
- Fold07.Rep5: alpha=0.8, lambda=0.2 
+ Fold07.Rep5: alpha=1.0, lambda=0.2 
- Fold07.Rep5: alpha=1.0, lambda=0.2 
+ Fold08.Rep5: alpha=0.0, lambda=0.2 
- Fold08.Rep5: alpha=0.0, lambda=0.2 
+ Fold08.Rep5: alpha=0.1, lambda=0.2 
- Fold08.Rep5: alpha=0.1, lambda=0.2 
+ Fold08.Rep5: alpha=0.2, lambda=0.2 
- Fold08.Rep5: alpha=0.2, lambda=0.2 
+ Fold08.Rep5: alpha=0.4, lambda=0.2 
- Fold08.Rep5: alpha=0.4, lambda=0.2 
+ Fold08.Rep5: alpha=0.6, lambda=0.2 
- Fold08.Rep5: alpha=0.6, lambda=0.2 
+ Fold08.Rep5: alpha=0.8, lambda=0.2 
- Fold08.Rep5: alpha=0.8, lambda=0.2 
+ Fold08.Rep5: alpha=1.0, lambda=0.2 
- Fold08.Rep5: alpha=1.0, lambda=0.2 
+ Fold09.Rep5: alpha=0.0, lambda=0.2 
- Fold09.Rep5: alpha=0.0, lambda=0.2 
+ Fold09.Rep5: alpha=0.1, lambda=0.2 
- Fold09.Rep5: alpha=0.1, lambda=0.2 
+ Fold09.Rep5: alpha=0.2, lambda=0.2 
- Fold09.Rep5: alpha=0.2, lambda=0.2 
+ Fold09.Rep5: alpha=0.4, lambda=0.2 
- Fold09.Rep5: alpha=0.4, lambda=0.2 
+ Fold09.Rep5: alpha=0.6, lambda=0.2 
- Fold09.Rep5: alpha=0.6, lambda=0.2 
+ Fold09.Rep5: alpha=0.8, lambda=0.2 
- Fold09.Rep5: alpha=0.8, lambda=0.2 
+ Fold09.Rep5: alpha=1.0, lambda=0.2 
- Fold09.Rep5: alpha=1.0, lambda=0.2 
+ Fold10.Rep5: alpha=0.0, lambda=0.2 
- Fold10.Rep5: alpha=0.0, lambda=0.2 
+ Fold10.Rep5: alpha=0.1, lambda=0.2 
- Fold10.Rep5: alpha=0.1, lambda=0.2 
+ Fold10.Rep5: alpha=0.2, lambda=0.2 
- Fold10.Rep5: alpha=0.2, lambda=0.2 
+ Fold10.Rep5: alpha=0.4, lambda=0.2 
- Fold10.Rep5: alpha=0.4, lambda=0.2 
+ Fold10.Rep5: alpha=0.6, lambda=0.2 
- Fold10.Rep5: alpha=0.6, lambda=0.2 
+ Fold10.Rep5: alpha=0.8, lambda=0.2 
- Fold10.Rep5: alpha=0.8, lambda=0.2 
+ Fold10.Rep5: alpha=1.0, lambda=0.2 
- Fold10.Rep5: alpha=1.0, lambda=0.2 
Aggregating results
Selecting tuning parameters
Fitting alpha = 0.1, lambda = 0.0587 on full training set
glmnetFit
glmnet 

891 samples
 53 predictor
  2 classes: 'Survived', 'Dead' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  alpha  lambda      ROC        Sens       Spec     
  0.0    0.01000000  0.8624351  0.7269580  0.8875152
  0.0    0.01487179  0.8624351  0.7269580  0.8875152
  0.0    0.01974359  0.8624351  0.7269580  0.8875152
  0.0    0.02461538  0.8624351  0.7269580  0.8875152
  0.0    0.02948718  0.8624351  0.7269580  0.8875152
  0.0    0.03435897  0.8626287  0.7269580  0.8871515
  0.0    0.03923077  0.8629192  0.7269412  0.8871515
  0.0    0.04410256  0.8631763  0.7269076  0.8860539
  0.0    0.04897436  0.8633421  0.7263361  0.8856902
  0.0    0.05384615  0.8631602  0.7246218  0.8860539
  0.0    0.05871795  0.8632236  0.7234286  0.8856902
  0.0    0.06358974  0.8634279  0.7240168  0.8853266
  0.0    0.06846154  0.8634157  0.7240168  0.8838721
  0.0    0.07333333  0.8634152  0.7246218  0.8835017
  0.0    0.07820513  0.8633267  0.7252269  0.8838653
  0.0    0.08307692  0.8633870  0.7246723  0.8842290
  0.0    0.08794872  0.8632672  0.7264370  0.8838653
  0.0    0.09282051  0.8630221  0.7258487  0.8838653
  0.0    0.09769231  0.8630112  0.7246723  0.8835017
  0.0    0.10256410  0.8630108  0.7258487  0.8827744
  0.0    0.10743590  0.8629793  0.7270252  0.8820337
  0.0    0.11230769  0.8629057  0.7275966  0.8820337
  0.0    0.11717949  0.8629160  0.7264370  0.8816700
  0.0    0.12205128  0.8629367  0.7252605  0.8816700
  0.0    0.12692308  0.8628505  0.7246723  0.8813064
  0.0    0.13179487  0.8627742  0.7252437  0.8813064
  0.0    0.13666667  0.8626347  0.7240840  0.8816700
  0.0    0.14153846  0.8626660  0.7234790  0.8816700
  0.0    0.14641026  0.8624861  0.7228908  0.8816700
  0.0    0.15128205  0.8623359  0.7205378  0.8809360
  0.0    0.15615385  0.8622288  0.7211261  0.8805724
  0.0    0.16102564  0.8621643  0.7211092  0.8798451
  0.0    0.16589744  0.8620677  0.7199496  0.8791111
  0.0    0.17076923  0.8620130  0.7193782  0.8787475
  0.0    0.17564103  0.8620221  0.7193782  0.8791111
  0.0    0.18051282  0.8620338  0.7193782  0.8791111
  0.0    0.18538462  0.8620142  0.7193782  0.8780202
  0.0    0.19025641  0.8620445  0.7182017  0.8765657
  0.0    0.19512821  0.8619043  0.7170252  0.8765657
  0.0    0.20000000  0.8618487  0.7152773  0.8758384
  0.1    0.01000000  0.8600372  0.7241176  0.8878519
  0.1    0.01487179  0.8615477  0.7264538  0.8896768
  0.1    0.01974359  0.8624891  0.7241345  0.8875017
  0.1    0.02461538  0.8631904  0.7223866  0.8878653
  0.1    0.02948718  0.8635738  0.7270252  0.8871246
  0.1    0.03435897  0.8640134  0.7281513  0.8867542
  0.1    0.03923077  0.8642901  0.7257983  0.8860269
  0.1    0.04410256  0.8644447  0.7257983  0.8852997
  0.1    0.04897436  0.8644893  0.7246218  0.8852997
  0.1    0.05384615  0.8647542  0.7234622  0.8845724
  0.1    0.05871795  0.8648624  0.7205378  0.8849360
  0.1    0.06358974  0.8646418  0.7205378  0.8849360
  0.1    0.06846154  0.8645368  0.7211261  0.8849360
  0.1    0.07333333  0.8643896  0.7193950  0.8842020
  0.1    0.07820513  0.8643594  0.7176303  0.8842020
  0.1    0.08307692  0.8642748  0.7141008  0.8845589
  0.1    0.08794872  0.8640778  0.7135294  0.8841953
  0.1    0.09282051  0.8636771  0.7123697  0.8834815
  0.1    0.09769231  0.8635362  0.7129580  0.8823906
  0.1    0.10256410  0.8631628  0.7123697  0.8805657
  0.1    0.10743590  0.8628222  0.7123529  0.8798316
  0.1    0.11230769  0.8625770  0.7106050  0.8794680
  0.1    0.11717949  0.8622052  0.7111933  0.8780135
  0.1    0.12205128  0.8619550  0.7111933  0.8772795
  0.1    0.12692308  0.8616472  0.7100336  0.8765522
  0.1    0.13179487  0.8616144  0.7088908  0.8761886
  0.1    0.13666667  0.8614748  0.7083025  0.8747340
  0.1    0.14153846  0.8612211  0.7082857  0.8747340
  0.1    0.14641026  0.8611024  0.7076975  0.8743636
  0.1    0.15128205  0.8609678  0.7065210  0.8740000
  0.1    0.15615385  0.8605709  0.7059328  0.8732727
  0.1    0.16102564  0.8604414  0.7059496  0.8725455
  0.1    0.16589744  0.8602385  0.7048067  0.8721751
  0.1    0.17076923  0.8599341  0.7036471  0.8725387
  0.1    0.17564103  0.8598033  0.7042353  0.8718114
  0.1    0.18051282  0.8595210  0.7036639  0.8707205
  0.1    0.18538462  0.8591656  0.7024874  0.8703569
  0.1    0.19025641  0.8589091  0.7018992  0.8699933
  0.1    0.19512821  0.8584617  0.7013277  0.8703569
  0.1    0.20000000  0.8581420  0.7013277  0.8692660
  0.2    0.01000000  0.8591220  0.7280672  0.8882020
  0.2    0.01487179  0.8604820  0.7245882  0.8903906
  0.2    0.01974359  0.8618651  0.7228571  0.8911178
  0.2    0.02461538  0.8620406  0.7228908  0.8892997
  0.2    0.02948718  0.8620432  0.7217143  0.8889360
  0.2    0.03435897  0.8622358  0.7234958  0.8889428
  0.2    0.03923077  0.8622652  0.7223361  0.8885859
  0.2    0.04410256  0.8620627  0.7229412  0.8889495
  0.2    0.04897436  0.8620116  0.7188235  0.8874949
  0.2    0.05384615  0.8619238  0.7153109  0.8864040
  0.2    0.05871795  0.8619083  0.7147227  0.8856700
  0.2    0.06358974  0.8614219  0.7141513  0.8853064
  0.2    0.06846154  0.8613769  0.7094790  0.8845724
  0.2    0.07333333  0.8613267  0.7089076  0.8849360
  0.2    0.07820513  0.8613301  0.7071429  0.8842088
  0.2    0.08307692  0.8613348  0.7065546  0.8823906
  0.2    0.08794872  0.8612440  0.7036471  0.8805724
  0.2    0.09282051  0.8607954  0.7036639  0.8791111
  0.2    0.09769231  0.8604293  0.7007563  0.8765657
  0.2    0.10256410  0.8599402  0.7007563  0.8747475
  0.2    0.10743590  0.8591161  0.7025378  0.8736431
  0.2    0.11230769  0.8580974  0.7019496  0.8721818
  0.2    0.11717949  0.8576971  0.7025042  0.8710774
  0.2    0.12205128  0.8571679  0.7025042  0.8703502
  0.2    0.12692308  0.8567174  0.7024874  0.8688956
  0.2    0.13179487  0.8561708  0.7007395  0.8656229
  0.2    0.13666667  0.8550908  0.7013277  0.8648956
  0.2    0.14153846  0.8544119  0.7007395  0.8645320
  0.2    0.14641026  0.8540218  0.7007563  0.8641684
  0.2    0.15128205  0.8534856  0.7007563  0.8638047
  0.2    0.15615385  0.8528506  0.7001681  0.8627138
  0.2    0.16102564  0.8523328  0.6984034  0.8627138
  0.2    0.16589744  0.8514521  0.6984034  0.8619865
  0.2    0.17076923  0.8508972  0.6978151  0.8598047
  0.2    0.17564103  0.8506594  0.6972269  0.8594411
  0.2    0.18051282  0.8500216  0.6978151  0.8583434
  0.2    0.18538462  0.8495585  0.6972437  0.8572458
  0.2    0.19025641  0.8490212  0.6966723  0.8561549
  0.2    0.19512821  0.8488122  0.6955126  0.8557912
  0.2    0.20000000  0.8477981  0.6949244  0.8547003
  0.4    0.01000000  0.8608629  0.7170924  0.8856835
  0.4    0.01487179  0.8630875  0.7188235  0.8875017
  0.4    0.01974359  0.8635522  0.7235126  0.8882290
  0.4    0.02461538  0.8638852  0.7241008  0.8863973
  0.4    0.02948718  0.8640961  0.7246387  0.8874882
  0.4    0.03435897  0.8636815  0.7205882  0.8878519
  0.4    0.03923077  0.8634645  0.7188403  0.8867542
  0.4    0.04410256  0.8625804  0.7165210  0.8845589
  0.4    0.04897436  0.8610777  0.7129916  0.8845589
  0.4    0.05384615  0.8603462  0.7106218  0.8812727
  0.4    0.05871795  0.8593766  0.7100168  0.8783569
  0.4    0.06358974  0.8586905  0.7106387  0.8758047
  0.4    0.06846154  0.8577189  0.7083193  0.8739865
  0.4    0.07333333  0.8561190  0.7071765  0.8707138
  0.4    0.07820513  0.8539432  0.7042521  0.8692525
  0.4    0.08307692  0.8520797  0.7048571  0.8674276
  0.4    0.08794872  0.8510941  0.7054454  0.8652391
  0.4    0.09282051  0.8498438  0.7060168  0.8612391
  0.4    0.09769231  0.8489821  0.7042521  0.8590572
  0.4    0.10256410  0.8485292  0.7054286  0.8543232
  0.4    0.10743590  0.8478570  0.7042521  0.8535960
  0.4    0.11230769  0.8474843  0.7013277  0.8514074
  0.4    0.11717949  0.8474952  0.7001345  0.8514141
  0.4    0.12205128  0.8472912  0.6995462  0.8503232
  0.4    0.12692308  0.8465502  0.6989580  0.8499596
  0.4    0.13179487  0.8458615  0.6977983  0.8495960
  0.4    0.13666667  0.8460465  0.6960336  0.8499596
  0.4    0.14153846  0.8456487  0.6936807  0.8510640
  0.4    0.14641026  0.8457106  0.6919328  0.8510640
  0.4    0.15128205  0.8463243  0.6884034  0.8514276
  0.4    0.15615385  0.8466792  0.6872269  0.8514276
  0.4    0.16102564  0.8467969  0.6860840  0.8514276
  0.4    0.16589744  0.8465443  0.6860840  0.8517912
  0.4    0.17076923  0.8460047  0.6854958  0.8521549
  0.4    0.17564103  0.8456889  0.6843193  0.8521549
  0.4    0.18051282  0.8452321  0.6843193  0.8521549
  0.4    0.18538462  0.8443937  0.6831765  0.8525185
  0.4    0.19025641  0.8439948  0.6831765  0.8525185
  0.4    0.19512821  0.8432182  0.6831765  0.8525185
  0.4    0.20000000  0.8429886  0.6820000  0.8525185
  0.6    0.01000000  0.8615146  0.7217311  0.8889428
  0.6    0.01487179  0.8625244  0.7234118  0.8911380
  0.6    0.01974359  0.8627385  0.7234622  0.8911246
  0.6    0.02461538  0.8630850  0.7211765  0.8900202
  0.6    0.02948718  0.8622451  0.7194286  0.8860000
  0.6    0.03435897  0.8620347  0.7135966  0.8830842
  0.6    0.03923077  0.8608737  0.7077311  0.8808956
  0.6    0.04410256  0.8595263  0.7077647  0.8776229
  0.6    0.04897436  0.8580028  0.7071765  0.8750774
  0.6    0.05384615  0.8563695  0.7054454  0.8725253
  0.6    0.05871795  0.8546962  0.7048739  0.8641616
  0.6    0.06358974  0.8528297  0.7066050  0.8601549
  0.6    0.06846154  0.8511820  0.7071765  0.8568822
  0.6    0.07333333  0.8498130  0.7089244  0.8521481
  0.6    0.07820513  0.8489291  0.7083361  0.8488687
  0.6    0.08307692  0.8492258  0.7071597  0.8488620
  0.6    0.08794872  0.8491236  0.7054118  0.8488620
  0.6    0.09282051  0.8492666  0.7018824  0.8488620
  0.6    0.09769231  0.8486469  0.7001176  0.8492256
  0.6    0.10256410  0.8485608  0.6954790  0.8506869
  0.6    0.10743590  0.8486213  0.6937143  0.8506869
  0.6    0.11230769  0.8478946  0.6931261  0.8506869
  0.6    0.11717949  0.8466370  0.6901849  0.8506869
  0.6    0.12205128  0.8461776  0.6860840  0.8517912
  0.6    0.12692308  0.8455451  0.6860840  0.8517912
  0.6    0.13179487  0.8436514  0.6854958  0.8517912
  0.6    0.13666667  0.8430642  0.6837647  0.8521549
  0.6    0.14153846  0.8423842  0.6831765  0.8521549
  0.6    0.14641026  0.8418510  0.6825882  0.8525185
  0.6    0.15128205  0.8407780  0.6820000  0.8525185
  0.6    0.15615385  0.8404541  0.6814118  0.8525185
  0.6    0.16102564  0.8401007  0.6814118  0.8525185
  0.6    0.16589744  0.8396466  0.6814118  0.8525185
  0.6    0.17076923  0.8391832  0.6814118  0.8525185
  0.6    0.17564103  0.8392284  0.6814118  0.8525185
  0.6    0.18051282  0.8390840  0.6814118  0.8525185
  0.6    0.18538462  0.8388220  0.6814118  0.8525185
  0.6    0.19025641  0.8389099  0.6814118  0.8525185
  0.6    0.19512821  0.8367532  0.6814118  0.8525185
  0.6    0.20000000  0.8350971  0.6814118  0.8525185
 [ reached getOption("max.print") -- omitted 80 rows ]

ROC was used to select the optimal model using  the largest value.
The final values used for the model were alpha = 0.1 and lambda = 0.05871795.
plot(glmnetFit)

Compare models

re <- resamples(x = list(xgb=xgbFit,xgbsmote=xgbsmoteFit,rf=rfFit.y,rfsmote=rfsmoteFit.y,gbm=boostFit))
summary(re)

Call:
summary.resamples(object = re)

Models: xgb, xgbsmote, rf, rfsmote, gbm 
Number of resamples: 50 

ROC 
              Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
xgb      0.7360963 0.8603304 0.8925134 0.8839215 0.9196505 0.9540260    0
xgbsmote 0.7462567 0.8653743 0.8867647 0.8800014 0.9078877 0.9350267    0
rf       0.7807487 0.8622670 0.8984301 0.8885360 0.9126833 0.9556150    0
rfsmote  0.7759358 0.8581818 0.8880026 0.8831274 0.9106551 0.9574866    0
gbm      0.7604278 0.8570834 0.8848587 0.8817104 0.9055828 0.9609626    0

Sens 
              Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
xgb      0.5588235 0.7058824 0.7352941 0.7388908 0.7941176 0.9142857    0
xgbsmote 0.4411765 0.5882353 0.6470588 0.6496134 0.7058824 0.8529412    0
rf       0.5294118 0.6764706 0.7352941 0.7286891 0.7941176 0.8823529    0
rfsmote  0.5294118 0.6470588 0.6857143 0.6983025 0.7647059 0.9117647    0
gbm      0.5588235 0.6907563 0.7647059 0.7532269 0.8235294 0.9117647    0

Spec 
              Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
xgb      0.7777778 0.8709596 0.8898990 0.8844916 0.9090909 0.9636364    0
xgbsmote 0.7818182 0.8909091 0.9272727 0.9169630 0.9454545 1.0000000    0
rf       0.8181818 0.8727273 0.8909091 0.8940202 0.9259259 0.9818182    0
rfsmote  0.8181818 0.8727273 0.8909091 0.9016498 0.9272727 0.9818182    0
gbm      0.8181818 0.8545455 0.8898990 0.8849091 0.9217172 0.9818182    0
bwplot(re)

compare_models(xgbFit,rfFit.y,metric = 'ROC')

    One Sample t-test

data:  x
t = -0.57365, df = 49, p-value = 0.5688
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.02077988  0.01155087
sample estimates:
   mean of x 
-0.004614506 
summary(diff(re))

Call:
summary.diff.resamples(object = diff(re))

p-value adjustment: bonferroni 
Upper diagonal: estimates of the difference
Lower diagonal: p-value for H0: difference = 0

ROC 
         xgb     xgbsmote   rf         rfsmote    gbm       
xgb               0.0039200 -0.0046145  0.0007941  0.0022111
xgbsmote 1.00000            -0.0085345 -0.0031259 -0.0017090
rf       1.00000 1.00000                0.0054086  0.0068256
rfsmote  1.00000 1.00000    0.03412                0.0014169
gbm      1.00000 1.00000    0.16388    1.00000              

Sens 
         xgb       xgbsmote  rf        rfsmote   gbm     
xgb                 0.08928   0.01020   0.04059  -0.01434
xgbsmote 7.907e-05           -0.07908  -0.04869  -0.10361
rf       1.0000000 7.109e-05            0.03039  -0.02454
rfsmote  0.2697866 0.0243587 0.0001129           -0.05492
gbm      1.0000000 7.618e-07 0.0116300 6.092e-07         

Spec 
         xgb      xgbsmote   rf         rfsmote    gbm       
xgb               -0.0324714 -0.0095286 -0.0171582 -0.0004175
xgbsmote 0.009900             0.0229428  0.0153131  0.0320539
rf       1.000000 0.290902              -0.0076296  0.0091111
rfsmote  0.335682 1.000000   0.153052               0.0167407
gbm      1.000000 0.018616   0.236054   0.005096

Evaluation

test.imp <- test.raw
#Embarked
test.imp$Embarked[is.na(test.imp$Embarked)]='S'
#Title
test.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = test.raw$Name)
#test.raw$title <- as.factor(test.raw$title)
test.imp$title <- as.character(test.raw$title)
test.imp$title[test.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
test.imp$title[test.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
test.imp$title[test.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
test.imp$title[test.imp$title %in% c('Ms','Mlle')] <- 'Miss'
test.imp$title <- as.factor(test.imp$title)
#Missing age
missing.age <- test.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
test.imp$Age[is.na(test.imp$Age)] <- age.predicted
test.imp$Age[test.imp$title=='Master' & test.imp$Age > 20] <- 4
#Child
test.imp$child <- 0
test.imp$child[test.imp$Age<18] <- 1
test.imp$almostadult <- as.numeric(between(test.imp$Age,16,18))
#Young/old
test.imp$Young <- ifelse(test.imp$Age<10,1,0)
test.imp$Seniors <- ifelse(test.imp$Age>60,1,0)
#Family Related
test.imp$TotalFam <- test.imp$SibSp + test.imp$Parch + 1
test.imp$Name <- NULL
test.imp$LargeParCh <- as.numeric(test.imp$Parch>=3)
test.imp$LargeSibSp <- as.numeric(test.imp$SibSp>=3)
test.imp$Single <- ifelse(test.imp$TotalFam==1,1,0)
test.imp$Couple <- ifelse(test.imp$TotalFam==2,1,0)
test.imp$Family <- ifelse(test.imp$TotalFam>2,1,0)
#Cabin & Deck
test.imp$CabinMissing <- as.numeric(is.na(test.raw$Cabin))
test.imp$CabinCode <- map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
test.imp$CabinCode[is.na(test.imp$CabinCode)] <- 'U'
test.imp$CabinNum <- as.numeric(map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
test.imp$CabinNum <- map_int(test.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
test.imp$CabinNum[is.na(test.imp$CabinNum)] <- 0
test.imp$TopDeck <- ifelse(test.imp$CabinCode %in% c('A','B'),1,0)
test.imp$MidDeck <- ifelse(test.imp$CabinCode %in% c('C','D'),1,0)
test.imp$LowerDeck <- ifelse(test.imp$TopDeck==0 & test.imp$MidDeck ==0 ,1,0)
test.imp$NumberofCabins <- map_int(test.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
test.imp$Cabin <- NULL
# Ticket
test.imp %<>%
    mutate(
      Ticket = str_to_upper(Ticket) %>%
          str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
      TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
      TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
      TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
      TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
      ) %>%
    mutate(
      TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
      TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
      TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
test.imp$Ticket <- NULL
test.imp$TicketNum <- NULL
#Fare
test.imp$Fare[is.na(test.imp$Fare)] <- 14.4542
test.imp$Fare[test.imp$Fare>232] <- 232
# boostPred <- predict(object = boostFit,
                     # newdata = test.imp)
dumV <- dummyVars(formula = ~.,data = test.imp)
Dtest <- predict(dumV,test.imp)
xgbPred <- predict(object = xgbFit,
                   newdata = Dtest)
rfPred <- predict(object = rfFit.y,
                   newdata = test.imp)
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : 
  factor TicketChar has new levels AQ, LP, STONOQ
PID <-
    readData(Titanic.path,
    test.data.file,
    test.column.types,
    missing.types)
PID <- PID$PassengerId
output <- write.csv(
    x = data.frame(
        PassengerId = PID,
        Survived = as.numeric(xgbPred)*-1+2
    ),
    file = 'aug30.csv',
    row.names = F
)

Conclusions

  1. I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I’ve also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.↩

  2. To iterate variable names in ggplot, use ggplot(...)+aes_string(...) in place of ggplot(...,aes(...)).↩

  3. Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf↩

---
title: "Kaggle Titanic Competition - Data Exploration & Model Building"
author: 'Rahul Sangole'
date: Aug 31, 2017
output:
  html_notebook:
    code_folding: hide
    collapsed: no
    fig_height: 3
    fig_width: 5
    highlight: pygments
    theme: journal
    toc: yes
    toc_float: yes
---
# Objectives

1. End to end analysis using R
2. Learn the caret package for ML
3. Learn to present the case using R Notebooks

***

# Read in the dataset
I stored the raw files on Github, so I used [RCurl](https://cran.r-project.org/web/packages/RCurl/index.html) with [Wehrley's method](https://github.com/wehrley/wehrley.github.io/blob/master/SOUPTONUTS.md) that utilizes read.csv to the fullest. It's one of the best ways I've found to read in data and also set data-types at the same time. He's done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.

```{r, message=FALSE, warning=FALSE}
library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)

readData <- function(path.name, file.name, column.types, missing.types) {
    gurl <- paste(path.name,file.name,sep="")
    download.file(gurl,file.name,method="curl",quiet = T)
    tbl_df(read.csv(file.name,colClasses=column.types,
             na.strings=missing.types))
}

Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer',   # PassengerId
                        'factor',    # Survived
                        'factor',    # Pclass
                        'character', # Name
                        'factor',    # Sex
                        'numeric',   # Age
                        'integer',   # SibSp
                        'integer',   # Parch
                        'character', # Ticket
                        'numeric',   # Fare
                        'character', # Cabin
                        'factor'     # Embarked
)

test.column.types <- train.column.types[-2]     # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)

prep_data <- function(D) {
    if (!is.null(D$Survived)) {
        D$Survived <- factor(D$Survived,
                             levels = c(1, 0),
                             labels = c('Survived', 'Dead'))
        }
    D$Pclass <- factor(D$Pclass,
                       levels = c(1, 2, 3),
                       labels = c('P1', 'P2', 'P3'))
    D$PassengerId <- NULL
    D
}

train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)
```

***

# Missing values analysis

Quick investigation of missing values can be done using the `complete.cases()`, and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have *some* missing data.

```{r}
#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)
```
Amelia lets us graphically investigate which variables have missing data. `purr::map_xxx()` gives this same information numerically in a succint fashion.
```{r, message=FALSE, warning=FALSE}
missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)
#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)
```
Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.

***

# EDA

The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:

* Target Variable
* Predictor Variables
    + Univariate
    + Bivariate
    + Multivariate

This gives me a structured approach towards larger datasets. My [professor](http://www.syamalasrinivasan.com/) at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data [^1]. [Anscombe](http://www.jstor.org/stable/2682899) (1973) clearly shows the importance of graphical analyses.

[^1]: I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I've also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.

## Target Variable
`Survived` is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.
```{r}
round(prop.table(table(train.raw$Survived)),2)
```

***

## Predictor Variables {.tabset .tabset-fade}

### Univariate & Bivariate

The first step is to look at every variable available. I prefer using the `ggplot2` framework for all the visuals.

#### Continuous Variables

* `Age` seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.

* `Fare` certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.

```{r, message=FALSE, warning=FALSE}
p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)
```

As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We'll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?

```{r}
summary(train.raw$Fare)
```

#### Categorical Variables

A ggplot command is iterated over for the categorical variables.[^2]

[^2]: To iterate variable names in ggplot, use `ggplot(...)+aes_string(...)` in place of `ggplot(...,aes(...))`.

Key takeways for the categorical variables:

1. `Pclass`: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.
2. `Pclass`: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.
3. `Sex`: Males have a very high fatality rate. Seems like the 'women and children' first policy was followed during evacuation.
4. `SibSp` & `Parch`: What's interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the 'women *and children*' policy being followed. (Or perhaps there weren't as many families with children on board!)
6. `Embarked`: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between `Embarked` and `Pclass` shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.

```{r, message=FALSE, warning=FALSE}
get_legend<-function(myggplot){
  tmp <- ggplot_gtable(ggplot_build(myggplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
            FUN = function(x) ggplot(data = train.raw)+
                aes_string(x=x,fill='Survived')+
                geom_bar(position="dodge")+
                theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
                 cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))
# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)
```

### Multivariate Analyses

Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find [beanplots](https://cran.r-project.org/web/packages/beanplot/beanplot.pdf) to be excellent complementary plots to boxplots (and in some cases, even better). They're a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.[^3]

Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?

1. The beanplot allows us to visualize the density function of the parameter, in this case: Age. Furthermore, the length of each beanline is cumulative to the number of datapoints that exist. Rightaway, we can tell that Pclass=3 has the most data in the set, with sparser data at Pclass=1.
2. The mean values for 1st class is higher than that for 2nd and 3rd class. The distributions of deceased and survived for 1st class are fairly similar.
3. For 2nd and 3rd class, the survived data shows a bimodal distribution. Bumps at the 0-10 age show that children were evacuated first. This is also the reason the mean values for survived is lower.
4. For 2nd and 3rd class, the deceased data shows a fairly normal distribution.
5. The individual measurements (represented by black lines) represent each observation and help identify outliers much more easily than a boxplot does.

[^3]: Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf

```{r, fig.height=3, fig.width=5, message=FALSE, warning=FALSE}
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()
beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
         border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
         main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)
```

A look into the `SibSp` and `Parch` variables shows something interesting. There are three regions one can identify:

* The probability of survival is minimal for number of parents/children aboard > 3.
* The probability of survival is minimal for number of siblings/spouses aboard > 3.
* For `SibSp`<=3 and `Parch`<=3, there are better chances for survival.

The grouping by `Pclass` reveals that all the large families were 3rd class travelers. Worse access to help... lowest chance for survival.

These could be simple rules either hard coded during model building: something along the lines of: *IF (SibSp>3 OR Parch >3) THEN prediction = 0*, or some derived variables can be created.

```{r}
ggplot(train.raw,aes(y=SibSp,x=Parch))+
    geom_jitter(aes(color=Survived,shape=Pclass))+
    theme_bw()+
    scale_shape(solid=F)+
    geom_vline(xintercept = 3,color='darkblue',lty=3)+
    geom_hline(yintercept = 3,color='darkblue',lty=3)
```

***

# Data Preparation

## Missing Values Imputation
Starting with the easier one first:

**Embarked**: The largest portion of the passengers embared at Southhampton. I'm replacing the NAs with the same. First, I create a new imputed training dataset.

```{r}
summary(train.raw$Embarked)
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)] <- 'S'
```

**Names, Titles & Age**:

The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles.  Here's the distribution of the titles by age. These can be used to impute the missing age values.

```{r, message=FALSE, warning=FALSE}
train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)

train.raw %>%
    na.omit() %>%
    group_by(title) %>%
    dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
    arrange(-Median_Age)
```

```{r, message=FALSE, warning=FALSE}
ggplot(train.raw,aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')
```

Grouping similar titles together, I've kept a few titles - Officer, Royalty, Mr, Mrs and Miss.

```{r}
train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)

train.imp %>%
    group_by(title) %>%
    summarise(Median_Age=median(Age,na.rm = T))
```

```{r}
ggplot(train.imp,aes(x=title,y=Age))+
    geom_jitter(shape=21,alpha=.6,col='blue')+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')
```

Now for the missing Age values. I'm trying out two strategies to impute age, just for kicks. First, a regression tree using the `rpart` method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of `maxdepth`. RMSE stablizes at a depth of 14, with a value of 12.2.

```{r}
age.predictors <- train.imp %>%
    dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
    filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
                     repeats = 5,
                     number = 200
                     )
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
                  data=age.predictors,
                  method='rpart2',
                  trControl = ctrl,
                  tuneGrid = rpartGrid
                  )
rpartFit
plot(rpartFit)
plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)
```

Another way is to run a randomforest with a search over values of `mtry` using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.

```{r}
set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5
                     )
rfFit <- train(Age~.,
                  data=age.predictors,
                  method='rf',
                  trControl = ctrl,
                  tuneGrid = rfGrid)
rfFit
plot(rfFit)
```

I'm going to use the randomForest model. Using the `predict.train()` to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of `Age`. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I'm going to force these to the median Age.

```{r}
missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
    mutate(AgeMissing = is.na(Age),
           Age = ifelse(AgeMissing,age.predicted,Age)) %>%
    ggplot(aes(x=title,y=Age))+
    stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
    geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
    labs(caption='Red points are median values')
train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)
```

## Derived Variables


**Child?:** Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)

```{r}
train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))
```

**Really young, or really old?:** Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.
```{r}
train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)
```


**Family related:** Let's also create some variables that talk about family sizes. What's the total family size -- continous variable `TotalFam`. Is the person single, part of a couple or a family? Three categorical variables for these.

```{r}
train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
# train.imp$LastName <- train.imp$Name %>% str_extract(pattern = '[a-zA-Z]+(?=,)')
# train.imp$FamSize <- paste0(train.imp$TotalFam,train.imp$LastName)
# train.imp$LastName <- NULL
train.imp$Name <- NULL
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>2,1,0)
```

**Cabin related:** Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that's important. Since lots of unknowns in the `Cabin` variable, all NA values are replaced by 'U'. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that's important too. Here, I create a bunch of categorical variables based off the original `Cabin`, and then remove it from the dataset.

```{r}
train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))

train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'

train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0

train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)

train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULL
```

**Ticket:** Lastly, the `ticket` variable. I'm not sure what to make of it, so I'm keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.

```{r}
train.imp$Ticket %>% table() %>% as.numeric() %>% table()
```

There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it's a very loose association.
```{r}
train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)
```

What I'm going to do is clean up the columns (remove special characters, spaces etc), then split the `Ticket` column into four: `TicketChar`, `TicketNum`,`TicketNumLength`, `TicketNumStart`.  (Upon running the script a few times, I've decided to get rid of `TicketNum`, but I'm commenting the code for future ref). The `TicketChar` variable as this distribution:

```{r}
train.imp %<>%
    mutate(
        Ticket = str_to_upper(Ticket) %>%
            str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
        TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
        TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
        TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
        TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+')) 
        ) %>%
     mutate(
         TicketChar = map_chr(.x=TicketChar,
                              .f=~str_split(string=.x, pattern = '',simplify = T)[1])
         ) %>%     
    mutate(
        TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
        TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
        TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar)
table(train.imp$TicketNumLen)
table(train.imp$TicketNumStart)
```

## Winzoring Variables

The `fare` variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.

```{r}
ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)
quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95))
train.imp$Fare[train.imp$Fare>232] <- 232
```

## Final Data Review

The dataset is now prepared for modeling. Here's a quick review of the data so far. 29 variables in total.

```{r}
train.imp %>% glimpse()
```


***

# Modeling

## Extreme Gradient Boosting (xgboost)
```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary
                     # sampling = 'smote'
                     )
set.seed(1)
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
xgbFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbFit
plot(xgbFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbFit,pch='|')
predict(xgbFit,type = 'raw') -> train.Class
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Extreme Gradient Boosting (xgboost) - SMOTE Sampling
```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
xgbGrid <- expand.grid(
    nrounds=c(2,3,4,5,6,7),
    max_depth=c(2,3,4,5,6,7),
    eta=c(0.3,0.5),
    gamma=1,
    colsample_bytree=1,
    min_child_weight=1,
    subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
set.seed(1)
xgbsmoteFit <- train(
    x=Dtrain,
    y=train.imp$Survived,
    method = 'xgbTree',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = xgbGrid,
    verbose = TRUE
)
xgbsmoteFit
plot(xgbsmoteFit)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)
xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()

densityplot(xgbsmoteFit,pch='|')
predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)
```

## Gradient Boosting (gbm)


```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                                          adaptive = list(min = 5, alpha = 0.05, 
                                             method = "gls", complete = TRUE),
                     search = 'random'
                     # sampling = 'smote'
                     )
gbmGrid <- expand.grid(
   n.trees=c(500,700,900,1100),
   interaction.depth=c(1,2,3),
   shrinkage=c(0.1,0.01),
   n.minobsinnode=10
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
set.seed(1)
boostFit <- train(
    x = Dtrain,
    y = boost.train$Survived,
    trControl=ctrl,
    method='gbm',
    tuneGrid=gbmGrid
)
boostFit
plot(boostFit)
xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')
plot(varImp(boostFit))
```

## Random Forest (rf)

```{r}
adapt_ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     adaptive = list(min = 5, alpha = 0.05, 
                                             method = "gls", complete = TRUE),
                     search = 'random'
                     )

rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfFit.y <- train(
    form = Survived~.,
    data = train.imp,
    method = 'rf',
    trControl = adapt_ctrl,
    # metric = "Kappa",
    tuneGrid = rfGrid,
    verbose = TRUE,
    ntree = 400
)
rfFit.y
plot(rfFit.y)
plot(rfFit.y$finalModel)


densityplot(rfFit.y,pch='|')
predict(rfFit.y,type = 'raw') -> train.rf.Class
predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)
```

## Random Forest (rf) - SMOTE

```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )

rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfsmoteFit.y <- train(
    form = Survived~.,
    data = train.imp,
    method = 'rf',
    trControl = ctrl,
    # metric = "Kappa",
    tuneGrid = rfGrid,
    verbose = TRUE,
    ntree = 400
)
rfsmoteFit.y
plot(rfsmoteFit.y)
plot(rfsmoteFit.y$finalModel)


densityplot(rfsmoteFit.y,pch='|')
predict(rfsmoteFit.y,type = 'raw') -> train.rfsmoteFit.Class
predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)
```

## Elastinet

```{r}
ctrl <- trainControl(method = "repeatedcv",
                     repeats = 5,
                     verboseIter = T,
                     classProbs = TRUE,
                     summaryFunction = twoClassSummary,
                     sampling = 'smote'
                     )
glmnetGrid <- expand.grid(.alpha = c(0,.1,.2,.4,.6,.8,1),
                          .lambda = seq(0.01,0.2,length.out = 40))
set.seed(1)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)
glmnetFit <- train(
    x = Dtrain,
    y = train.imp$Survived,
    trControl=ctrl,
    method='glmnet',
    tuneGrid=glmnetGrid
)
glmnetFit
plot(glmnetFit)
```


## Compare models

```{r}
re <- resamples(x = list(xgb=xgbFit,xgbsmote=xgbsmoteFit,rf=rfFit.y,rfsmote=rfsmoteFit.y,gbm=boostFit))
summary(re)
bwplot(re)
compare_models(xgbFit,rfFit.y,metric = 'ROC')
summary(diff(re))
```


# Evaluation

```{r}
test.imp <- test.raw

#Embarked
test.imp$Embarked[is.na(test.imp$Embarked)]='S'

#Title
test.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = test.raw$Name)
#test.raw$title <- as.factor(test.raw$title)
test.imp$title <- as.character(test.raw$title)
test.imp$title[test.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
test.imp$title[test.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
test.imp$title[test.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
test.imp$title[test.imp$title %in% c('Ms','Mlle')] <- 'Miss'
test.imp$title <- as.factor(test.imp$title)

#Missing age
missing.age <- test.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
test.imp$Age[is.na(test.imp$Age)] <- age.predicted
test.imp$Age[test.imp$title=='Master' & test.imp$Age > 20] <- 4

#Child
test.imp$child <- 0
test.imp$child[test.imp$Age<18] <- 1
test.imp$almostadult <- as.numeric(between(test.imp$Age,16,18))

#Young/old
test.imp$Young <- ifelse(test.imp$Age<10,1,0)
test.imp$Seniors <- ifelse(test.imp$Age>60,1,0)

#Family Related
test.imp$TotalFam <- test.imp$SibSp + test.imp$Parch + 1
test.imp$Name <- NULL
test.imp$LargeParCh <- as.numeric(test.imp$Parch>=3)
test.imp$LargeSibSp <- as.numeric(test.imp$SibSp>=3)
test.imp$Single <- ifelse(test.imp$TotalFam==1,1,0)
test.imp$Couple <- ifelse(test.imp$TotalFam==2,1,0)
test.imp$Family <- ifelse(test.imp$TotalFam>2,1,0)

#Cabin & Deck
test.imp$CabinMissing <- as.numeric(is.na(test.raw$Cabin))
test.imp$CabinCode <- map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
test.imp$CabinCode[is.na(test.imp$CabinCode)] <- 'U'
test.imp$CabinNum <- as.numeric(map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
test.imp$CabinNum <- map_int(test.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
test.imp$CabinNum[is.na(test.imp$CabinNum)] <- 0

test.imp$TopDeck <- ifelse(test.imp$CabinCode %in% c('A','B'),1,0)
test.imp$MidDeck <- ifelse(test.imp$CabinCode %in% c('C','D'),1,0)
test.imp$LowerDeck <- ifelse(test.imp$TopDeck==0 & test.imp$MidDeck ==0 ,1,0)

test.imp$NumberofCabins <- map_int(test.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
test.imp$Cabin <- NULL


# Ticket
test.imp %<>%
    mutate(
      Ticket = str_to_upper(Ticket) %>%
          str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
      TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
      TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
      TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
      TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
      ) %>%
    mutate(
      TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
      TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
      TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
    )
test.imp$Ticket <- NULL
test.imp$TicketNum <- NULL

#Fare
test.imp$Fare[is.na(test.imp$Fare)] <- 14.4542
test.imp$Fare[test.imp$Fare>232] <- 232

```



```{r}
# boostPred <- predict(object = boostFit,
                     # newdata = test.imp)

dumV <- dummyVars(formula = ~.,data = test.imp)
Dtest <- predict(dumV,test.imp)

xgbPred <- predict(object = xgbFit,
                   newdata = Dtest)
rfPred <- predict(object = rfFit.y,
                   newdata = test.imp)
```


```{r}
PID <-
    readData(Titanic.path,
    test.data.file,
    test.column.types,
    missing.types)
PID <- PID$PassengerId
output <- write.csv(
    x = data.frame(
        PassengerId = PID,
        Survived = as.numeric(xgbPred)*-1+2
    ),
    file = 'aug30.csv',
    row.names = F
)



```

# Conclusions
